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scTE

Quantifying transposable element (TEs) expression from single-cell sequencing data

scTE takes as input:

Aligned sequence reads (BAM/SAM format)
The genomic location of TEs (BED format)
The genomic location of genes (GTF format)

scTE workflow

Installation

scTE works with python >=3.6.

$ git clone https://github.com/JiekaiLab/scTE.git
$ cd scTE
$ python setup.py install

Usage

Building genome indices<br> scTE builds genome indices for the fast alignment of reads to genes and TEs. These indices can be automatically generated using the commands:

$ scTE_build -g mm10 # Mouse
$ scTE_build -g hg38 # Human
$ scTE_build -g panTro6 # Chimpanzee
$ scTE_build -g macFas5 # Macaca fascicularis
$ scTE_build -g dm6 # Drosophila melanogaster
$ scTE_build -g danRer11 # Zebrafish
$ scTE_build -g xenTro9 # Xenopus tropicalis

These scripts will automatically download the genome annotations, for mouse:

$ ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M21/gencode.vM21.annotation.gtf.gz
$ http://hgdownload.soe.ucsc.edu/goldenPath/mm10/database/rmsk.txt.gz

Or for human:

$ ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_30/gencode.v30.annotation.gtf.gz
$ http://hgdownload.soe.ucsc.edu/goldenPath/hg38/database/rmsk.txt.gz

Or for Chimpanzee:

$ http://ftp.ensembl.org/pub/release-103/gtf/pan_troglodytes/Pan_troglodytes.Pan_tro_3.0.103.gtf.gz
$ https://hgdownload.soe.ucsc.edu/goldenPath/panTro6/database/rmsk.txt.gz

Or for Macaca fascicularis:

$ http://ftp.ensembl.org/pub/release-102/gtf/macaca_fascicularis/Macaca_fascicularis.Macaca_fascicularis_5.0.102.gtf.gz
$ http://hgdownload.soe.ucsc.edu/goldenPath/macFas5/database/rmsk.txt.gz

Or for Drosophila melanogaster:

$ http://ftp.ensembl.org/pub/release-103/gtf/drosophila_melanogaster/Drosophila_melanogaster.BDGP6.32.103.gtf.gz
$ http://hgdownload.soe.ucsc.edu/goldenPath/dm6/database/rmsk.txt.gz

Or for Zebrafish:

$ http://ftp.ensembl.org/pub/release-103/gtf/danio_rerio/Danio_rerio.GRCz11.103.gtf.gz
$ https://hgdownload.soe.ucsc.edu/goldenPath/danRer11/database/rmsk.txt.gz

Or for Xenopus tropicalis:

$ http://ftp.ensembl.org/pub/release-103/gtf/xenopus_tropicalis/Xenopus_tropicalis.Xenopus_tropicalis_v9.1.103.gtf.gz
$ https://hgdownload.soe.ucsc.edu/goldenPath/xenTro9/database/rmsk.txt.gz

mm10, hg38, panTro6, macFas5, dm6, danRer11, xenTro9 is the genome assembly version. If you want to use your customs reference, you can use the -gene -te options:

scTE_build -te TEs.bed -gene Genes.gtf -o custome

-te
    Six columns bed file for transposable elements annotation.
-gene
    Gtf file for genes annotation.

For more informat about BED and GTF format, see from UCSC. These annotations are then processed and converted into genome indices. The scTE algorithm will allocate reads first to gene exons, and then to TEs by default. Hence TEs inside exon/UTR regions of genes annotated in GENCODE will only contribute to the gene, and not to the TE score. This feature can be changed by setting –mode/-m inclusive in scTE, which will instruct scTE to assign the reads to both TEs and genes if a read comes from a TE inside exon/UTR regions of genes. If you want to remove the TEs inside the intron of genes, you can sete –mode/-m nointron in scTE

Analysis of 10x style scRNA-seq data

scTE makes BAM/SAM file as input, highly recommend to use unfiltered alignment file as input.

For bam file generated by STARsolo etc, the cell barcodes and UMI need to be integrated into the read 'CR:Z' or 'UR:Z' tage as bellow:

$ scTE -i inp.bam -o out -x mm10.exclusive.idx --hdf5 True -CB CR -UMI UR

$ samtools view test.bam
A00269:12:H7YF2DMXX:2	0	chr10	55902580	255	50M	*	0	0	GTTCTCTCCGTATGTGAGCATGGGAGATACATCCCAGAAAGGCAGAAGGG	FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF	NH:i:1	HI:i:1	AS:i:49	nM:i:0	CR:Z:CTAGAGTGTTTCGCTC	CY:Z:FFFFFFFFFFFFFFFF	UR:Z:TACATGACGC	UY:Z:FFFFFFFFFF
A00269:13:H7YF2DMXX:2	0	chr10	55902784	255	50M	*	0	0	ATAATCTTTGAGATCTCTGGTGAAAATAAGTAGCATAAAGGACAGAATCA	FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF	NH:i:1	HI:i:1	AS:i:49	nM:i:0	CR:Z:CTAGAGTGTTTCGCTC	CY:Z:FFFFFFFFFFFFFFFF	UR:Z:TACATGACGC	UY:Z:FFFFFFFFFF
A00269:14:H7YF2DMXX:2	0	chr13	67837311	255	50M	*	0	0	CTGTTCATTATTTGAGGAAATCAGGACAGGAAATCAAACATGGCAGAATC	FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF	NH:i:1	HI:i:1	AS:i:49	nM:i:0	CR:Z:ATCGAGTGTTTCGCTC	CY:Z:FFFFFFFFFFFFFFFF	UR:Z:TACATGACGC	UY:Z:FFFFFFFFFF
A00269:15:H7YF2DMXX:2	0	chr14	114380523	255	50M	*	0	0	GATCCAGATTAATTGAGACTGTTGATCCTCCTACAGGGTCGCCCTTCTCC	FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF	NH:i:1	HI:i:1	AS:i:49	nM:i:0	CR:Z:CTAGAGTGTTTCGCTC	CY:Z:FFFFFFFFFFFFFFFF	UR:Z:TACATGACGC	UY:Z:FFFFFFFFFF

For bam file generated by Cell Ranger etc, the cell barcodes and UMI need to be integrated into the read 'CB:Z' or 'UB:Z' tage as bellow:

$ scTE -i inp.bam -o out -x mm10.exclusive.idx --hdf5 True -CB CB -UMI UB

$ samtools view test.bam
A00519:758:HTCCHDSXY:3:2535:21296:19774	16	chr1	14021	0	90M	*	0	0	TGGATTTCTATCTCCCTGGCTTGGTGCCAGTTCCTCCAAGTCGATGGCACCTCCCTCCCTCTCAACCACTTGAGCAAACTCCAAGACATC	,FFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:F:FFFFFFFFFFFFFFFFFFF:FFFFF	NH:i:5	HI:i:1	AS:i:88	nM:i:0	RG:Z:SC3_v3_NextGem_DI_CellPlex_Human_PBMC_10K:0:1:HTCCHDSXY:3	RE:A:I	xf:i:0	CR:Z:CTCCCTCCACTGCGAC	CY:Z:FFFFFFFFFFFFFFFF	CB:Z:CTCCCTCCACTGCGAC-1	UR:Z:AAGGCGTAGTAG	UY:Z:FFFFFFFFFFFF	UB:Z:AAGGCGTAGTAG
A00519:758:HTCCHDSXY:1:1355:17237:31720	0	chr1	14260	0	90M	*	0	0	CTCCCTCTCATCCCAGAGAAACAGGTCAGCTGGGAGCTTCTGCCCCCACTGCCTAGGGACCAACAGGGGCAGGAGGCAGTCACTGACCCC	FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF	NH:i:5	HI:i:1	AS:i:88	nM:i:0	RG:Z:SC3_v3_NextGem_DI_CellPlex_Human_PBMC_10K:0:1:HTCCHDSXY:1	RE:A:I	xf:i:0	CR:Z:TCGTCCACAGTATGAA	CY:Z:FFFFFFFFFFFFFFFF	CB:Z:TCGTCCACAGTATGAA-1	UR:Z:GACTTATTTTTT	UY:Z:FFFFFFFFFFFF	UB:Z:GACTTATTTTTT
A00519:758:HTCCHDSXY:3:2227:16703:32080	16	chr1	14411	1	90M	*	0	0	TCAGTTCTTTATTGATTGGTGTGCCGTTTTCTCTGGAAGCCTCTTAAGAACACAGTGGCGCAGGCTGGGTGGAGCCGTCCCCCCATGGAG	FFFFFFFFFFFFFFFFFFFFFFFFFFF:FFFF:FFFFFFFF:FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF	NH:i:3	HI:i:1	AS:i:88	nM:i:0	RG:Z:SC3_v3_NextGem_DI_CellPlex_Human_PBMC_10K:0:1:HTCCHDSXY:3	RE:A:I	xf:i:0	CR:Z:TTGAGTGGTTGTGGCC	CY:Z:FFFFFFFFFFFFFFFF	CB:Z:TTGAGTGGTTGTGGCC-1	UR:Z:TATAATGCTCAG	UY:Z:FFFFFFFFFFFF	UB:Z:TATAATGCTCAG
A00519:758:HTCCHDSXY:3:2563:23665:33802	16	chr1	14411	1	90M	*	0	0	TCAGTTCTTTATTGATTGGTGTGCCGTTTTCTCTGGAAGCCTCTTAAGAACACAGTGGCGCAGGCTGGGTGGAGCCGTCCCCCCATGGAG	FFFFF:FFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FFFFFFFF:FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF	NH:i:3	HI:i:1	AS:i:88	nM:i:0	RG:Z:SC3_v3_NextGem_DI_CellPlex_Human_PBMC_10K:0:1:HTCCHDSXY:3	RE:A:I	xf:i:0	CR:Z:TGTTGAGAGGCAATGC	CY:Z:FFFFFFFFFFFFFFFF	CB:Z:TGTTGAGAGGCAATGC-1	UR:Z:ACGGGTGTGGAG	UY:Z:FFFFFFFFFFFF	UB:Z:ACGGGTGTGGAG

-i
    Input file: BAM/SAM file from CellRanger or STARsolo
-o
    Output file prefix
-x
    The filename of the index for the reference genome annotation generated by scTE_build
-p
    Number of threads to use, Default: 1. scTE takes ~10Gb memory each thread for human and mouse genome.
--hdf5
    Save the output as .h5ad formatted file instead of csv file. Default: False

scTE is most tuned to STARsolo or the Cell Ranger pipeline outputs, and can accept BAM files produced by either of these two programs. For other aligners, the barcode should be stored in the CR:Z or CB:Z tag, and the UMI in the UR:Z or UB:Z tag in the BAM file

Analysis of C1 style scRNA-seq data<br> If the UMI is missing or not used in the scRNA-seq technology (for example on the Fluidigm C1 platform), it can be disabled with –UMI False (the default is True) switch in scTE. If the barcode is missing it can be disabled with the –CB False (the default is True), and instead the cell barcodes will be taken from the names of the BAM files.

$ scTE -i inp.bam -o out -x mm10.exclusive.idx -CB False -UMI False

multiple BAM files can be provided to scTE with the –i option

$ scTE -i *.bam -o out -x mm10.exclusive.idx -CB False -UMI False

$ scTE -i input1.bam,input2.bam,... -o out -x mm10.exclusive.idx -CB False -UMI False

Analysis of scATAC-seq data<br> The genome indices were prebuilt using:

$ wget -c http://hgdownload.soe.ucsc.edu/goldenPath/mm10/database/rmsk.txt.gz -O mm10.te.txt.gz
$ zcat mm10.te.txt.gz | grep -E 'LINE|SINE|LTR|Retroposon' | cut -f6-8,11 >mm10.te.bed
$ scTEATAC_build -g mm10.te.bed -o mm10.te.atac

Then the bam file can processe using scTE with the command:

scTEATAC -i input.bam -x mm10.te.atac.idx

Citation<br> If scTE is useful for your research, consider citing Nature Communications (2021)