Description

These tracks represent data from cerebral cortex organoid differentiation assays in four primate species as described in Field, et al., 2018. Cerebral organoids were generated from human, chimpanzee, orangutan, and rhesus macaque pluripotent stem cells using an optimized version of the Eiraku et al., 2008 protocol. RNA was collected from weekly time points over 5-7 weeks and subjected to total transcriptome RNA-seq. Coverage tracks were normalized using DESeq size factors. The raw files and processed data data are available on GEO (GSE106245).

Each track indicates the genomic coverage from strand-specific RNAseq data (on either the plus or minus strand) in a genome assembly relevant to a specific primate species, at one of the time points of the organoid differentiation assay. The coverage has been normalized between samples as described in Methods below. The names of the samples are the same as those used in the GEO accession, where the name-prefix indicates the sample source and timepoint, and the name-suffix is a unique identifier that distinguishes among biological replicates:

• human: hg19
  • huESC -- human embryonic stem cells
  • hEBw1 -- human cortical organoid week1
  • hEBw2 -- human cortical organoid week2
  • hEBw3 -- human cortical organoid week3
  • hEBw4 -- human cortical organoid week4
  • hEBw5 -- human cortical organoid week5
  • hEBw6 -- human cortical organoid week6
  • hEBw7 -- human cortical organoid week7
• chimpanzee: panTro4
  • cIPw0 -- chimpanzee induced pluripotent stem cells
  • cIPw1 -- chimpanzee cortical organoids week1
  • cIPw2 -- chimpanzee cortical organoids week2
  • cIPw3 -- chimpanzee cortical organoids week3
  • cIPw4 -- chimpanzee cortical organoids week4
  • cIPw5 -- chimpanzee cortical organoids week5
• orangutan: ponAbe2
  • oIPw0 -- orangutan induced pluripotent stem cells
  • oIPw1 -- orangutan cortical organoids week1
  • oIPw2 -- orangutan cortical organoids week2
  • oIPw3 -- orangutan cortical organoids week3
  • oIPw4 -- orangutan cortical organoids week4
  • oIPw5 -- orangutan cortical organoids week5
• rhesus macaque: rheMac8
  • rhESC -- rhesus macaque embryonic stem cells
  • rEBw1 -- rhesus macaque cortical organoids week1
  • rEBw2 -- rhesus macaque cortical organoids week2
  • rEBw3 -- rhesus macaque cortical organoids week3
  • rEBw4 -- rhesus macaque cortical organoids week4
  • rEBw5 -- rhesus macaque cortical organoids week5

Display Conventions and Configuration

The minus strand coverage tracks use negative values so that they descend from the zero line. The plus strand coverage tracks use positive values. The colors have been chosen to be colorblind-friendly:

•  Blue  - plus strand coverage
•  Red  - minus strand coverage

Because the coverage values have been normalized between all the samples, the visual display indicates the relative expression between samples at a locus as long as all the individual tracks use the same "Vertical viewing range" scale.

Since there is wide variation in coverage between genes with different levels of expression, you should adjust the "Vertical viewing range" control at the composite track level in order to vertically zoom in and out at a given locus. In general, you should probably keep the plus and minus sets of tracks at the same "Vertical viewing range" scale. However, you might also want to use different plus and minus scales to more closely examine cases of anti-sense transcription. Although you can adjust each sample's "Vertical viewing range" separately, this will distort the relative expression of that sample, so should be avoided. If you do this inadvertantly, the "Reset to defaults" function can be used to restore all the individual track settings.

Because the plus and minus strand are aggregated into separate composite tracks, the default browser display groups them separately. Be aware that you can drag the tracks individually to reorder them. For example, you might want to place each sample's plus and minus strands together, with plus above minus for a more natural display.

Methods

The full description of data processing of the RNAseq data can be found in Field, et al., 2018. Here is a brief synopsis.

Trimming and Filtering

The raw paired-end reads were trimmed to eliminate low quality bases. The trimmed reads were mapped with Bowtie2 (Langmead et al., 2012) to a set of repeat-elements for the appropriate species. Reads mapping to these elements were removed from further processing.

Alignment and Duplicate Removal

The filtered reads were aligned to the appropriate genome assembly with STAR (Dobin et al., 2012) keeping only the primary mapping for multiply-mapped paired-end reads. Duplicate mappings were removed with Samtools.

Coverage and DESEQ Normalization

The duplicate-removed alignments were converted to coverage using bedTools. The total coverage at all the exonic positions of a gene was divided by the read length (sum of the length of the two paired-end reads) for input to DESEQ2. As part of its differential expression analysis, DESEQ2 performs a normalization across all samples using the expression of all genes (Love et al., 2014). This normalization compensates for differences in sequencing depth between the samples. It comprises a set of "sizeFactor" values. The un-normalized values are divided by the sizeFactors before the rest of the DESEQ2 algorithm is performed. In the same way, the coverage values from bedTools have been divided by the sizeFactor values to create the tracks presented here.

Data were generated and processed at the UC Santa Cruz Genomics Institute. For inquiries, please contact us at the following address: ssalama@ucsc. edu

References

Field AR, Jacobs FMJ, Fiddes IT, Phillips APR, Reyes-Ortiz AM, LaMontagne E, Whitehead L, Meng V, Rosenkrantz JL, Olsen M, Hauessler M, Katzman S, Salama SR, Haussler D. Structurally conserved primate lncRNAs are transiently expressed during human cortical differentiation and influence cell type specific genes. Stem Cell Reports. 2018. (In Press)

Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., and Gingeras, T.R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1), 15-21.

Eiraku, M., Watanabe, K., Matsuo-Takasaki, M., Kawada, M., Yonemura, S., Matsumura, M., Wataya, T., Nishiyama, A., Muguruma, K., and Sasai, Y. (2008). Self-organized formation of polarized cortical tissues from ES cells and its active manipulation by extrinsic signals. Cell Stem Cell 3, 519-532.

Langmead, B., and Salzberg, S. (2012). Fast gapped-read alignment with Bowtie 2. Nature Methods 9, 357-359.

Love, M.I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15, 550.