How-To Use uap¶
At first, you need to install uap (see Installation of uap).
Try Existing Configurations¶
After you have done that you need a working configuration file.
Example configurations are included in uap‘s installation directory.
They are stored inside the
Go there and try:
$ uap index_mycoplasma_genitalium_ASM2732v1_genome.yaml status
Start your first uap analysis showcasing the controlled indexing of a genome (arguably a tiny one):
$ uap index_mycoplasma_genitalium_ASM2732v1_genome.yaml status [uap] Set log level to ERROR [uap][ERROR]: index_mycoplasma_genitalium_ASM2732v1_genome.yaml: Destination path does not exist: genomes/bacteria/Mycoplasma_genitalium/
destination_path does not exist. (see Destination_path Section)
Create it and start again:
$ mkdir genomes/bacteria/Mycoplasma_genitalium/ $ uap index_mycoplasma_genitalium_ASM2732v1_genome.yaml status Waiting tasks ------------- [w] bowtie2_index/Mycoplasma_genitalium_index-download [w] bwa_index/Mycoplasma_genitalium_index-download [w] fasta_index/download [w] segemehl_index/Mycoplasma_genitalium_genome-download Ready tasks ----------- [r] M_genitalium_genome/download tasks: 5 total, 4 waiting, 1 ready
If you still do get errors, please check that the tools defined in
index_mycoplasma_genitalium_ASM2732v1_genome.yaml are available in your
environment (see Tools Section).
[w] stands for a waiting status of a task and the
[r] stands for a ready status of a task. (see Command-Line Usage of uap)
Go on and try some more example configurations (let’s for now assume that all tools are installed and configured correctly). You want to create indexes of the human genome (hg19):
$ uap index_homo_sapiens_hg19_genome.yaml status [uap] Set log level to ERROR [uap][ERROR]: Output directory (genomes/animalia/chordata/mammalia/primates/homo_sapiens/hg19/chromosome_sizes) does not exist. Please create it. $ mkdir genomes/animalia/chordata/mammalia/primates/homo_sapiens/hg19/chromosome_sizes $ uap index_homo_sapiens_hg19_genome.yaml run-locally <Analysis starts>
Create Your Own Configuration¶
Although writing the configuration may seem a bit complicated, the trouble pays off later because further interaction with the pipeline is quite simple. The structure and content of the configuration files is very detailed described on another page (see Configuration File). Here is a simple configuration:
Insert YAML here!
General Structure of Sequencing Analysis¶
Every analysis of high-throughput sequencing data evolves around some basic tasks. Irrespective of sequencing RNA or DNA.
- Get the sequencing reads as input (most likely fastq.gz)
- Remove adapter sequences from your sequencing reads
- Align the sequencing reads onto the reference genome
The After these steps are finished a lot of different analysis could be applied on the data. Furtheron example configurations for often used analyses are shown. The enumeration of steps show continues as if the basic steps were already performed.
RNAseq analysis often aims at the discovery of differentially expressed (known) transcripts. Therefore mappped reads for at least two different samples have to be available.
- Get annotation set (for e.g. genes, transcripts, ...)
- Count the number of reads overlapping the annotation
- Perform statistical analysis, based on counts
Assemble novel transcripts¶
As the publicly available annotations, e.g. from GENCODE, are probably not complete, the assembly of novel transcripts from RNAseq data is another task one would perform to investigate the transcriptome.
ChIPseq analysis aims at the discovery of genomic loci at which protein(s) of interest were bound. The experiment is an enrichment procedure using specific antibodies. The enrichment detection is normally performed by so called peak calling programs.
- Get negative control
- Peak calling
Prepare UCSC genome browser tracks¶
The conversion of sequencing data into an format that can be displayed by the UCSC genome browser is needed in almost all sequencing projects.