output - Oshlack/necklace GitHub Wiki
- superTranscriptome/SuperDuper.fasta - the assembled superTranscriptome sequences for each gene
- mapped_reads/<sample_name>.bam - reads mapped back to the superTranscriptome for each sample
- counts/blocks.gtf - the segmentation of superTranscripts based on splice junctions (similar to exons)
These files can be used for example, to visualise read coverage across a gene in IGV. To do this, load SuperDuper.fasta as the reference sequence using "Load Genome from File". Then load the sample bams and gtf as usual ("Load from file").
- counts/gene.counts - gene-level counts for use in differential gene expression testing
- counts/block.counts - block-level counts for use in differential transcript usage testing
For follow on analyses, please refer the relevant bioconductor packages. For example: edgeR, voom or DESeq for gene expression testing or for differential transcript usage: diffSpliceDGE (in edgeR), diffSplice (in voom) and DEXSeq
The count tables can be used exactly like the "count.txt" tables generated from genome-only based analysis, such as those created by featureCounts for which there are many examples on the internet. Here are a few examples of R code to get you started with analysing the count data: Differential gene expression with EdgeR, Differential transcript usage with DEXSeq, Differential transcript usage with DiffSplice
- stats/gene_info.txt - Number of genes found in the reference annotation, genome-based superTranscriptome (reference annotation and genome-guided assembly), and full superTranscriptome (reference annotation, genome-guided assembly and de novo assembly)
- stats/mapping_info.txt - As above, but for the number of reads aligning
- stats/size_info.txt - As above, but for the number of base pairs