title: Maternal slimming oocytes, RNA-Seq

city: Beijing

first: yanchang

last: wei

address: #1 Beichen West Rd

institute: Institute of Zoology, Chinese Academy of Sciences

description: mRNA

data_processing:
Base-calling software, version, parameters: SOLiD4.0 SAT pipeline, default parameters.
All sequenced reads were aligned to the mouse genome sequences (ENSEMBL56 ) using AB's SOLiD Corona_lite_v4.2 software available at http://solidsoftwaretools.com/gf/project/corona/. We used a recursive strategy to improve the usable sequenced reads information: 50mers reads were first mapped to the genome with at most 5 color-space mismatches and reads that failed to be mapped were progressively trimmed off, five bases at one time from the 3' end, and mapped to the genome again until a match was found (unless the read had been trimmed by <30 bases). All those uniquely mapped reads were used for calculating the genes' RPKM values (reads per kilobase of exon per million mapped sequenced reads), which is used to define gene expression level. We identified differentially expressed genes (DEGs) from different samples according to an R package named DEGseq.
Genome_build: mm9
Supplementary_files_format_and_content: GSE40478_output_score.txt: Tab-delimited text file that includes RPKM values for each sample. 'GSE40478_output_score.txt' is linked to the GSE40478 record as a supplementary file.

source_name: oocytes, maternal slimming

growth_protocol: Female F0 founders were first raised in a standard laboratory environment and became obese at 12 weeks of age. They were then placed into EE cages or remained in the normal cage for 4 weeks. At 16 weeks, naturally ovulated mature oocytes were collected from 3 control and 3 slimming female F0 founders (3 oocytes per mouse, 9 oocytes for each group).

extract_protocol: Each group of 9 oocytes were lysed in one tube and the total mRNAs were reverse transcribed to cDNAs by universe primer 1 (UP1) adaptor primers containing 24 bp oligo(dT) sequence. The remaining primers were removed and poly(A) tails to the 3' terminal of the cDNAs were added. Universe primer 2 (UP2) primers containing 24 bp oligo(dT) were used for the synthesis of the second cDNA strand. The double-strand cDNA library was amplified by UP1 and UP2 for 18 cycles, and by amine-blocked UP1 and UP2 for 14 cycles. The final amplified cDNA libraries were sent to the Genome Institute of Beijing and sequenced by the Applied Biosystem SOLiD 4 sequencing system.

treatment_protocol: Mothers slimming vs. control, analyze the oocyte transcriptome using RNA-Seq analysis.

value: ICR

value: oocyte at MII stage

tag: maternal age

value: 16 weeks

tag: treatment

value: enriched environment-induced maternal slimming


title: Maternal control oocytes, RNA-Seq

status: Public on Aug 30 2012

type: SRA

country: China

description: mRNA

library_source: transcriptomic

molecule: total RNA

organism: Mus musculus

source_name: oocytes, maternal control

tag: strain

tag: tissue

value: mother fed standard diet


title: Brdt_Input_Spermatocytes

city: La Tronche

first: Sophie

last: Rousseaux

address: Bd de la Chantourne

department: IAB

country: France

institute: INSERM-UGA

library_source: genomic

data_processing:
Sequence reads of 50+35 bp reads were produced with a SOLiDTM 4 sequencer, according to standard Life Technologies protocols. The sequence reads were analyzed with the BioScope™ software suite (cms_4448431.pdf).
After sequence read mapping to version 9 build 37 of the mouse genome allowing up to 4 mismatches, only reads mapping once to the genome were retained for subsequent analyses, to prevent spurious calls in repetitive regions.
Four BAM files were generated by the Bioscope pipeline, corresponding to the two biological conditions (P, Brdt ChIP-seq from premeiotic spermatocytes and R, Brdt ChIP-seq from postmeiotic, round spermatids), and their respective sonicated DNA inputs.
BAM files were transformed into SAM format (Li et al, 2009; PMID: 19505943).
Genome_build: mm9
Supplementary_files_format_and_content: The aligned reads files were then processed using SICER (Garmire et al, 2011, PMID:21980340; Zang et al, 2009; PMID:19505939) for input substraction and peaks detection. Island filtered peaks were detected in spermatocytes and round spermatids with the following parameters: Redundancy threshold: 1;  Window size: 200bp; Fragment size: 150bp; Gap size: 600bp; FDR: 0.01.

molecule: genomic DNA

source_name: Male germ cells

extract_protocol:
Chromatin immunoprecipitation (ChIP) on spermatocytes and round spermatids: Ten C57BL/6 mice were used for each germ cells fractionation experiment. Five testes were used in each fractionation experiment, and the respective fractions corresponding to spermatocytes and round spermatids from the 10 mice were pooled for subsequent experiments. Nuclei were prepared and MNAse digestion was carried out on nuclei to release chromatin fibers, as previously described (Catena et al. 2008; Biol Reprod. 2009 Feb;80(2):358-66.).    Digestion was performed to obtain chromatin fragments of approximately 200bp (mononucleosomes). Each ChIP was performed on 500µg of chromatin, with 0.6µg of antibody in LSDB 250mM buffer and using ProteinG coated Dynabeads.
Spermatogenic cell suspensions were fractionated using a BSA gradient and pooled fraction of spermatocytes and round spermatids were used for ChIP with an anti-Brdt exactly as previously reported in (Tan et al, 2011; PMID: 21925322).
Standard Applied Biosystems Life Technologies protocols were followed for the creation of short-insert bar-coded, paired-end libraries (insert length ~150 bp) according to the ChIP-seq Kit Guide from the manufacturer (cms_079323.pdf) and library preparation guide (cms_081748.pdf).

tag: cell type

value: Spermatocytes

tag: genotype

value: wild type

tag: cell stage

value: meiotic

tag: chip antibody


title: Brdt_ChipSeq_Round spermatids

value: Round spermatids

value: post-meiotic

value: anti-Brdt [Immunopurified rabbit anti-Brdt produced against the recombinant protein.]


title: Brdt_ChipSeq_Spermatocytes


title: K562_human

status: Public on Aug 29 2012

city: Woodbury

first: Philippe

last: Batut

state: NY

address: 500 Sunnyside Blvd.

country: USA

institute: CSHL

description:
RNA (5'-complete cDNAs selected by TEX digest, template-switching and cap-trapping).
RNA-Seq (RAMPAGE)

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode, allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5’ ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

K562_RAMPAGE_+.bw: bigWig coverage by cDNA 5' ends (+ strand).
K562_RAMPAGE_-.bw: bigWig coverage by cDNA 5' ends (- strand).
K562_All_RAMPAGE_peaks.bed: BED file describing all RAMPAGE peaks.
K562_GeneTSS_RAMPAGE_peaks.bed: BED file describing all peaks attributed to annotated genes.

Genome Build:
K562_RAMPAGE_+.bw: hg19
K562_RAMPAGE_-.bw: hg19
K562_All_RAMPAGE_peaks.bed: hg19
K562_GeneTSS_RAMPAGE_peaks.bed: hg19

organism: Homo sapiens

source_name: Human K562 cell line

growth_protocol: Cells were grown in RPMI medium with 10% FBS in a 37ºC incubator with 5% CO2. See ATCC website for details.

extract_protocol: Total RNA was extracted with Trizol (Invitrogen) according to the manufacturer's instructions and treated with DNaseI (Roche). We checked on a Bioanalyzer (Agilent) that the RNA was of very high quality. 5’-monophosphate species -- mainly ribosomes -- were depleted by TEX digest (Epicentre). Our 5’-complete cDNA synthesis and selection strategy relies on the combination of two orthogonal enrichment methods: reverse-transcriptase template-switching, and cap-trapping. The template-switching approach is based on the ability of reverse-transcriptase to add linker sequences to the ends of 5’-complete cDNAs -- preferentially if they are made from capped transcripts. Cap-trapping relies on the biotinylation of capped RNA molecules and specific pulldown of their associated 5’-complete cDNAs. The library was run on a DNA HS Bioanalyzer chip for quality control, quantified by quantitative PCR, and sequenced on one lane  on an Illumina GAIIx (2x76bp). Please see Supplemetary Material of the original publication for a detailed protocol.

treatment_protocol: None.

tag: cell line


title: Dmel35_AdM5d

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel35_AdM5d_-.bw: dm3
Dmel35_AdM5d_+.bw: dm3

organism: Drosophila melanogaster

source_name: Whole organism, adult

growth_protocol: Stocks of the y; cn bw sp strain were maintained in standard cornmeal medium bottles in a 24ºC incubator. Embryo collections were performed in population cages (Flystuff, #59-116). 2- to 7-day-old flies were left to acclimatize to the cage for at least 48h and regularly fed with grape juice-agar plates (Flystuff, #47-102) generously loaded with yeast paste. After two 2-hour pre-lays, embryos were collected in 1-hour windows and aged appropriately (24 timepoints, 0-24h). Embryos were washed with deionized water, dechorionated for 90 sec with 50% bleach, rinsed abundantly with water, and snap-frozen in liquid nitrogen. Larvae and pupae were collected as described previously (Graveley, Brooks et al. 2010). For L1 and L2 stages, 2-hour embryo collections were aged for 42 or 66 hours, larvae were briefly rinsed with deionized water and snap-frozen. For L3 stages, embryos were transferred to bottles containing cornmeal medium supplemented with 0.05% bromophenol blue, and wandering L3 larvae were staged based on gut staining (dark, light or clear gut) and snap-frozen. For pupae, 2-hour embryo collections were transferred to standard cornmeal medium bottles, the positions of new white prepupae on the walls of the bottle were marked, and pupae were collected and snap-frozen at the desired age. For adults, 0- to 12-hour-old flies were sexed and kept in vials with cornmeal medium for 5 days, and then snap-frozen.

extract_protocol:
RNA extraction: Total RNA was extracted from adult flies using Trizol (Invitrogen) according to the manufacturer's instructions and treated with DNaseI (Roche). Extraction from embryos, larvae and pupae was performed using the RNAdvance Tissue kit (Agencourt #A32649) according to the manufacturer's instructions, including DNaseI treatment. We systematically checked on a Bioanalyzer (Agilent) that the RNA was of very high quality. 5'monophosphate species --mainly ribosomes -- were depleted by TEX digest.

Library preparation & sequencing: Three multiplexed libraries were prepared: one for embryos (24 barcoded samples), one for larvae and pupae (10 samples), and one for adults (2 samples). The reverse-transcription was run in parallel for all samples destined to the same library, and the samples were pooled right after reverse-transcription. Our 5'-complete cDNA selection strategy relies on the combination of two orthogonal enrichment methods: reverse-transcriptase template-switching, and cap-trapping. The template-switching approach is based on the ability of reverse-transcriptase to add linker sequences to the ends of 5'-complete cDNAs -- preferentially if they are made from capped transcripts. Cap-trapping relies on the biotinylation of capped RNA molecules and specific pulldown of their associated 5'-complete cDNAs. The libraries were run on a DNA HS Bioanalyzer chip for quality control, quantified by quantitative PCR, and sequenced on one lane each on an Illumina GAIIx (adults, 2x76bp) or HiSeq (embryos, larvae and pupae, 2x101bp). Please see Supplemetary Material of the original publication for a detailed protocol.

value: y; cn bw sp

value: whole body

tag: developmental stage

value: adults 5 days post-eclosion

tag: gender

value: male


title: mRRBS 95

city: Cambridge

first: Kendell

last: Clement

state: MA

address: 7 Divinity Ave.

institute: Harvard University

data_processing:
Alignment: bisulfite-converted reads were aligned to the hg19 human genome using MAQ in bisulfite alignment mode using the following parameters: -D -s 0 -M c -e 100
Genome_build: hg19
Supplementary_files_format_and_content: Bed files contain the number of methylated and unmethylated reads for covered CpGs

source_name: mRRBS library 95

extract_protocol: mRRBS was performed according to the mRRBS protocol (Boyle et al. 2012)

tag: mrrbs library


title: mRRBS 94

source_name: mRRBS library 94


title: ctrl_trik4_r2_rep1

city: Barcelona

first: David

last: Rossell

address: Baldiri Reixac 10

country: Spain

institute: IRB Barcelona

description: Input DNA from LID RNAi knock-down dm (in a mutant background).

data_processing:
Alignment files: FASTQ files were obtained with the standard Illumina Pipeline version 1.5.1. Reads were aligned to the dm3 genome with Bowtie version 0.12.5, keeping sequences mapping to a unique location in the genome with up to 2 mismatches in the first 28 bases. We removed PCR over-amplification artifacts by only keeping the first 100 appearances of a given sequence for analysis. We removed strand-specific biases with a procedure analogous to that proposed for the MACS algorithm.

Peak files: We defined peaks using a two-step procedure. Step 1: We found enriched genomic regions showing high accumulation of sequences in the IP sample compared to the control. We selected regions with coverage above 10 and compared the proportion of reads inside/outside of each region between the IP sample and its corresponding control with a logistic regression likelihood-ratio test. We defined enriched regions as those with Benjanimi-Yekutieli adjusted P-value < 0.05. Step 2: We defined peaks as location within the enriched regions (determined in Step 1) with coverage above 50. Bioconductor software was used for all data analysis unless where otherwise specified.

source_name: wing imaginal discs, LID RNAi knock-down, input

growth_protocol: w1118, act5Cgal4>LID RNAi or lidk06801;act5Cgal4>lid RNAi larvae were grown at 25ºC until they became wall-climbing third instar larvae.

extract_protocol: Discs were crosslinked in 1.8% formaldehyde solution and sonicated. DNA-protein complexes were immunoprecipitated with anti-LID or anti-H3K4me3 antibodies. Commercial Abcam ab8580 antibodies were used as anti-H3K4me3. Custom-made anti-LID polyclonal antibodies were raised in rabbits from GST fusion proteins containing fragments of LID, and IgGs were purified for the ChIP experiments. Libraries were prepared according to Illumina's instructions accompanying the ChIP-Seq DNA Sample Prep Kit (Part# IP-102-1001). Briefly, DNA was end-repaired using a combination of T4 DNA polymerase, E. coli DNA Pol I large fragment (Klenow polymerase) and T4 polynucleotide kinase. The blunt, phosphorylated ends were treated with Klenow fragment (32 to 52 exo minus) and dATP to yield a protruding 3- 'A' base for ligation of Illumina's adapters, which have a single 'T' base overhang at the 3’ end. After adapter ligation, DNA was PCR amplified with Illumina primers for 16 cycles and library fragments of ~250 bp (insert plus adaptor and PCR primer sequences) were band isolated from an agarose gel. The purified DNA was captured on an Illumina flow cell for cluster generation. Libraries were sequenced for 40 (H3K4me3) and 36 (LID) nucleotides on the Illumina Genome Analyzer II following the manufacturer's protocols.

treatment_protocol: The LID RNAi strain was obtained from NIG-FLY and it was combined with lidk06801. For every sample, around 500 wing imaginal discs were dissected in PBS.

value: LID RNAi

value: third instar larvae

value: wing imaginal discs


title: Foxa2 ChIP-seq

first: Chenhuan

last: Xu

address: 1 Beichen West Road

institute: Beijing Institute of Genomics

data_processing:
Basecalls performed using CASAVA.
Sequence reads were mapped to the mouse genome (mm9) using SOAP (Li et al., 2008) with max 4 mismatches.
Reads that mapped to unique genomic location were retained for further analysis.
Model-based Analysis of ChIP-seq (MACS) algorithm was used to identify the enriched peaks with P-value < 0.001 (Zhang et al., 2008).
Genome_build: mm9
Supplementary_files_format_and_content: BED format. Model-based Analysis of ChIP-seq (MACS) algorithm was used to identify the enriched peaks with P-value < 0.001 (Zhang et al., 2008). The peaks were then mapped to ±50kb of transcription start site (TSS) of nearest RefSeq genes to define the Foxa2 target genes.

source_name: Definitive endoderm cells

extract_protocol: ChIP-Seq libraries were constructed using the Illumina ChIP-Seq library preparation kit according to the manufacturer’s instructions (Illumina, San Diego, CA). Briefly, about 1 to 5 ng of ChIP DNAs were end-repaired to produce blunt-ended fragment, then 3-dA overhang was produced by adding Klenow exo- with dATP. After this step, Illumina adaptors were ligated and run on a 2% agarose gel to remove excess adaptors. An 18-cycle PCR amplification was carried out using the gel-purified template to enrich for final sequencing library.

value: ES-derived definitive endoderm cells

tag: chip antibody vendor

value: Santa Cruz

tag: chip antibody cat. #


title: mRRBS 93

source_name: mRRBS library 93


title: mRRBS 92

source_name: mRRBS library 92


title: Brdt_Input_Round spermatids


title: mRRBS 86

source_name: mRRBS library 86


title: mRRBS 87

source_name: mRRBS library 87


title: mRRBS 88

source_name: mRRBS library 88


title: mRRBS 84

source_name: mRRBS library 84


title: mRRBS 85

source_name: mRRBS library 85


title: mRRBS 81

source_name: mRRBS library 81


title: mRRBS 91

source_name: mRRBS library 91


title: Dmel36_AdF5d

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel36_AdF5d_-.bw: dm3
Dmel36_AdF5d_+.bw: dm3

value: female


title: mRRBS 83

source_name: mRRBS library 83


title: mRRBS 96

source_name: mRRBS library 96


title: mRRBS 76

source_name: mRRBS library 76


title: mRRBS 78

source_name: mRRBS library 78


title: mRRBS 77

source_name: mRRBS library 77


title: mRRBS 82

source_name: mRRBS library 82


title: mRRBS 72

source_name: mRRBS library 72


title: mRRBS 73

source_name: mRRBS library 73


title: mRRBS 74

source_name: mRRBS library 74


title: mRRBS 70

source_name: mRRBS library 70


title: mRRBS 68

source_name: mRRBS library 68


title: mRRBS 75

source_name: mRRBS library 75


title: mRRBS 69

source_name: mRRBS library 69


title: mRRBS 71

source_name: mRRBS library 71


title: mRRBS 67

source_name: mRRBS library 67


title: mRRBS 89

source_name: mRRBS library 89


title: mRRBS 66

source_name: mRRBS library 66


title: mRRBS 80

source_name: mRRBS library 80


title: mRRBS 57

source_name: mRRBS library 57


title: mRRBS 64

source_name: mRRBS library 64


title: mRRBS 59

source_name: mRRBS library 59


title: mRRBS 65

source_name: mRRBS library 65


title: mRRBS 58

source_name: mRRBS library 58


title: mRRBS 79

source_name: mRRBS library 79


title: mRRBS 63

source_name: mRRBS library 63


title: mRRBS 56

source_name: mRRBS library 56


title: mRRBS 55

source_name: mRRBS library 55


title: mRRBS 54

source_name: mRRBS library 54


title: Dmel34_WPP+5d

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel34_WPP+5d_-.bw: dm3
Dmel34_WPP+5d_+.bw: dm3

source_name: Whole organism, pupae

value: WPP + 5 day


title: mRRBS 53

source_name: mRRBS library 53


title: Dmel32_WPP+3d

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel32_WPP+3d_-.bw: dm3
Dmel32_WPP+3d_+.bw: dm3

value: WPP + 3 day


title: mRRBS 62

source_name: mRRBS library 62


title: mRRBS 61

source_name: mRRBS library 61


title: Dmel31_WPP+2d

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel31_WPP+2d_-.bw: dm3
Dmel31_WPP+2d_+.bw: dm3

value: WPP + 2 day


title: mRRBS 60

source_name: mRRBS library 60


title: mRRBS 52

source_name: mRRBS library 52


title: Dmel30_WPP+1d

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel30_WPP+1d_-.bw: dm3
Dmel30_WPP+1d_+.bw: dm3

value: WPP + 1 day


title: Dmel29_L3_CG

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel29_L3_CG_-.bw: dm3
Dmel29_L3_CG_+.bw: dm3

source_name: Whole organism, larvae

value: larvae l3 (clear gut stage)


title: mRRBS 50

source_name: mRRBS library 50


title: mRRBS 49

source_name: mRRBS library 49


title: Dmel28_L3_LG

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel28_L3_LG_-.bw: dm3
Dmel28_L3_LG_+.bw: dm3

value: larvae l3 (light gut stage)


title: mRRBS 47

source_name: mRRBS library 47


title: mRRBS 48

source_name: mRRBS library 48


title: mRRBS 45

source_name: mRRBS library 45


title: mRRBS 46

source_name: mRRBS library 46


title: Dmel25_L1

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel25_L1_-.bw: dm3
Dmel25_L1_+.bw: dm3

value: larvae l1


title: mRRBS 44

source_name: mRRBS library 44


title: mRRBS 42

source_name: mRRBS library 42


title: Dmel26_L2

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel26_L2_-.bw: dm3
Dmel26_L2_+.bw: dm3

value: y; cn bw sp

value: larvae l2


title: Dmel24_E24h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel24_E24h_-.bw: dm3
Dmel24_E24h_+.bw: dm3

source_name: Whole organism, embryos

value: whole body

value: embryos 23-24h after egg laying


title: mRRBS 43

source_name: mRRBS library 43


title: Dmel27_L3_DG

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel27_L3_DG_-.bw: dm3
Dmel27_L3_DG_+.bw: dm3

value: larvae l3 (dark gut stage)


title: mRRBS 39

source_name: mRRBS library 39


title: mRRBS 51

source_name: mRRBS library 51


title: Dmel22_E22h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel22_E22h_-.bw: dm3
Dmel22_E22h_+.bw: dm3

value: embryos 21-22h after egg laying


title: Dmel21_E21h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel21_E21h_-.bw: dm3
Dmel21_E21h_+.bw: dm3

value: embryos 20-21h after egg laying


title: Dmel20_E20h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel20_E20h_-.bw: dm3
Dmel20_E20h_+.bw: dm3

value: embryos 19-20h after egg laying


title: mRRBS 40

data_processing:
Alignment: bisulfite-converted reads were aligned to the hg19 human genome using MAQ in bisulfite alignment mode using the following parameters: -D -s 0 -M c -e 100
Genome_build: hg19
Supplementary_files_format_and_content: Bed files contain the number of methylated and unmethylated reads for covered CpGs

source_name: mRRBS library 40


title: Dmel23_E23h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel23_E23h_-.bw: dm3
Dmel23_E23h_+.bw: dm3

value: embryos 22-23h after egg laying


title: Dmel18_E18h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel18_E18h_-.bw: dm3
Dmel18_E18h_+.bw: dm3

value: embryos 17-18h after egg laying


title: Dmel19_E19h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel19_E19h_-.bw: dm3
Dmel19_E19h_+.bw: dm3

value: embryos 18-19h after egg laying


title: Dmel17_E17h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel17_E17h_-.bw: dm3
Dmel17_E17h_+.bw: dm3

value: embryos 16-17h after egg laying


title: mRRBS 41

source_name: mRRBS library 41


title: Dmel33_WPP+4d

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel33_WPP+4d_-.bw: dm3
Dmel33_WPP+4d_+.bw: dm3

source_name: Whole organism, pupae

value: WPP + 4 day


title: mRRBS 36

source_name: mRRBS library 36


title: mRRBS 37

source_name: mRRBS library 37


title: Dmel16_E16h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel16_E16h_-.bw: dm3
Dmel16_E16h_+.bw: dm3

value: embryos 15-16h after egg laying


title: mRRBS 35

source_name: mRRBS library 35


title: mRRBS 38

source_name: mRRBS library 38


title: Dmel14_E14h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel14_E14h_-.bw: dm3
Dmel14_E14h_+.bw: dm3

value: embryos 13-14h after egg laying


title: Dmel15_E15h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel15_E15h_-.bw: dm3
Dmel15_E15h_+.bw: dm3

value: embryos 14-15h after egg laying


title: mRRBS 33

source_name: mRRBS library 33


title: Dmel11_E11h

data_processing:
Sequencing reads alignment: The sequences corresponding to the library identification barcode (first 6 bases of read 1) and the reverse-transcription primer (first 15 bases of read 2) were trimmed prior to mapping. Trimmed reads were mapped with STAR, with parameters described in Tables S2-3 of the original publication. All uniquely mapping reads were kept. As a rescue strategy for multiply mapping reads, if all alignments for those reads started within an annotated transposon and overlapped the same gene annotation, the alignment starting in the closest transposon insertion was selected. All non-rescued multi-mappers were discarded.

Data analysis pipeline: PCR duplicates, defined as reads sharing the same alignment coordinates (start, end and splice sites), were removed from the individual datasets. To avoid over-collapsing, we took advantage of the fact that the long random sequence (15-mer) of our reverse-transcription primer often primes with mismatches. We used this sequence as a pseudo-random barcode allowing us to distinguish between true duplicates (same barcode) and independent identical inserts. All collapsed datasets were then combined prior to peak calling. The density of cDNA 5' ends across the genome was determined from this combined dataset, as well as the density of coverage by second (i.e., downstream) sequencing reads. Peaks were called by a sliding window algorithm that assesses the significance of local signal enrichment given a null distribution. Downstream read coverage in the same window was used to correct for local transcript abundance, by subtracting from the raw signal a pseudocount proportional to this coverage. After FDR correction, significant windows in close proximity to each other were merged into peaks, and those were trimmed at the edges down to the first base with signal. (Parameters: window width 15 bases, null distribution negative binomial with k=4, background weight 0.5, FDR 0.01, merging range 150 bases). These peaks were connected to annotated genes based on cDNA structure information. For each peak, if we could find at least 2 inserts having their 5' in the peak and overlapping an annotated exon of a gene, the peak was functionally linked to that gene. If a peak could potentially be linked to several genes, ties were broken by removing all links that were 5-fold weaker than the strongest one. For quantification, the signal for each peak and each timepoint was derived from the uncollapsed datasets, and normalized to dataset size (defined as the total number of reads attributed to any genic TSS). We built partial transcript models by running Cufflinks separately on the set of reads coming from each peak for each given dataset, and collapsing all transcripts for each peak using Cuffmerge. For a more detailed description of the analysis pipeline, please refer to the original publication.

*_+.bw file: bigWig coverage by cDNA 5' ends (+ strand).
*_-.bw file: bigWig coverage by cDNA 5' ends (- strand).
Genome Build:
Dmel11_E11h_-.bw: dm3
Dmel11_E11h_+.bw: dm3

value: embryos 10-11h after egg laying


title: mRRBS 32

source_name: mRRBS library 32