STAR.align.folder: Align all libraries in folder with STAR
In Roleren/ORFik: Open Reading Frames in Genomics

STAR.align.folder

R Documentation

Align all libraries in folder with STAR

Description

Does either all files as paired end or single end, so if you have mix, split them in two different folders.
If STAR halts at .... loading genome, it means the STAR index was aborted early, then you need to run: STAR.remove.crashed.genome(), with the genome that crashed, and rerun.

Usage

STAR.align.folder(
  input.dir,
  output.dir,
  index.dir,
  star.path = STAR.install(),
  fastp = install.fastp(),
  paired.end = FALSE,
  steps = "tr-ge",
  adapter.sequence = "auto",
  quality.filtering = FALSE,
  min.length = 20,
  mismatches = 3,
  trim.front = 0,
  max.multimap = 10,
  alignment.type = "Local",
  allow.introns = TRUE,
  max.cpus = min(90, BiocParallel::bpparam()$workers),
  wait = TRUE,
  include.subfolders = "n",
  resume = NULL,
  multiQC = TRUE,
  keep.contaminants = FALSE,
  keep.contaminants.type = c("bam", "fastq")[1],
  keep.unaligned.genome = FALSE,
  script.folder = system.file("STAR_Aligner", "RNA_Align_pipeline_folder.sh", package =
    "ORFik"),
  script.single = system.file("STAR_Aligner", "RNA_Align_pipeline.sh", package = "ORFik")
)

Arguments

`input.dir`	path to fast files to align, the valid input files will be search for from formats: (".fasta", ".fastq", ".fq", or ".fa") with or without compression of .gz. Also either paired end or single end reads. Pairs will automatically be detected from similarity of naming, separated by something as .1 and .2 in the end. If files are renamed, where pairs are not similarily named, this process will fail to find correct pairs!
`output.dir`	directory to save indices, default: paste0(dirname(arguments[1]), "/STAR_index/"), where arguments is the arguments input for this function.
`index.dir`	path to STAR index folder. Path returned from ORFik function STAR.index, when you created the index folders.
`star.path`	path to STAR, default: STAR.install(), if you don't have STAR installed at default location, it will install it there, set path to a runnable star if you already have it.
`fastp`	path to fastp trimmer, default: install.fastp(), if you have it somewhere else already installed, give the path. Only works for unix (linux or Mac OS), if not on unix, use your favorite trimmer and give the output files from that trimmer as input.dir here.
`paired.end`	a logical: default FALSE, alternative TRUE. If TRUE, will auto detect pairs by names. Can not be a combination of both TRUE and FALSE! If running in folder mode: The folder must then contain an even number of files and they must be named with the same prefix and sufix of either _1 and _2, 1 and 2, etc. If SRR numbers are used, it will start on lowest and match with second lowest etc.
`steps`	a character, default: "tr-ge", trimming then genome alignment steps of depletion and alignment wanted: The posible candidates you can use are: tr : trim reads co : contamination merged depletion ph : phix depletion rR : rrna depletion nc : ncrna depletion tR : trna depletion (Mature tRNA, so no intron checks done) ge : genome alignment all: run steps: "tr-co-ge" or "tr-ph-rR-nc-tR-ge", depending on if you have merged contaminants or not If not "all", a subset of these ("tr-co-ph-rR-nc-tR-ge") If co (merged contaminants) is used, non of the specific contaminants can be specified, since they should be a subset of co. The step where you align to the genome is usually always included, unless you are doing pure contaminant analysis or only trimming. For Ribo-seq and TCP(RCP-seq) you should do rR (ribosomal RNA depletion), so when you made the STAR index you need the rRNA step, either use rRNA from .gtf or manual download. (usually just download a Silva rRNA database for SSU&LSU at: https://www.arb-silva.de/) for your species.
`adapter.sequence`	character, default: "auto". Auto detect adapter using fastp adapter auto detection, checking first 1.5M reads. (Auto detection of adapter will not work 100% of the time (if the library is of low quality), then you must rerun this function with specified adapter from fastp adapter analysis. , using FASTQC or other adapter detection tools, else alignment will most likely fail!). If already trimmed or trimming not wanted: adapter.sequence = "disable" .You can manually assign adapter like: "ATCTCGTATGCCGTCTTCTGCTTG" or "AAAAAAAAAAAAA". You can also specify one of the three presets: illumina (TrueSeq ~75/100 bp sequencing): AGATCGGAAGAGC small_RNA (standard for ~50 bp sequencing): TGGAATTCTCGG nextera: CTGTCTCTTATA Paired end auto detection uses overlap sequence of pairs, to use the slower more secure paired end adapter detection, specify as: "autoPE".
`quality.filtering`	logical, default FALSE. Not needed for modern library prep of RNA-seq, Ribo-seq etc (usually < ~ 0.5 If you are aligning bad quality data, set this to TRUE. These filters will then be applied (default of fastp), filter if: Number of N bases in read: > 5 Read quality: > 40% of bases in the read are <Q15
`min.length`	20, minimum length of aligned read without mismatches to pass filter. Anything under 20 is dangerous, as chance of random hits will become high!
`mismatches`	3, max non matched bases. Excludes soft-clipping, this only filters reads that have defined mismatches in STAR. Only applies for genome alignment step.
`trim.front`	0, default trim 0 bases 5'. For Ribo-seq use default 0. Ignored if tr (trim) is not one of the arguments in "steps"
`max.multimap`	numeric, default 10. If a read maps to more locations than specified, will skip the read. Set to 1 to only get unique mapping reads. Only applies for genome alignment step. The depletions are allowing for multimapping.
`alignment.type`	default: "Local": standard local alignment with soft-clipping allowed, "EndToEnd" (global): force end-to-end read alignment, does not soft-clip.
`allow.introns`	logical, default TRUE. Allow large gaps of N in reads during genome alignment, if FALSE: sets –alignIntronMax to 1 (no introns). NOTE: You will still get some spliced reads if you assigned a gtf at the index step.
`max.cpus`	integer, default: `min(90, BiocParallel:::bpparam()$workers)`, number of threads to use. Default is minimum of 90 and maximum cores - 2. So if you have 8 cores it will use 6. Note: FASTP will use maximum 16 threads as from testing I see performance actually degrades using anything higher. From testing I also see STAR gets no performance gain after ~50 threads. I do suspect this will change when hard drives gets better in the future.
`wait`	a logical (not `NA`) indicating whether the R interpreter should wait for the command to finish, or run it asynchronously. This will be ignored (and the interpreter will always wait) if `intern = TRUE`. When running the command asynchronously, no output will be displayed on the `Rgui` console in Windows (it will be dropped, instead).
`include.subfolders`	"n" (no), do recursive search downwards for fast files if "y".
`resume`	default: NULL, continue from step, lets say steps are "tr-ph-ge": (trim, phix depletion, genome alignment) and resume is "ge", you will then use the assumed already trimmed and phix depleted data and start at genome alignment, useful if something crashed. Like if you specified wrong STAR version, but the trimming step was completed. Resume mode can only run 1 step at the time.
`multiQC`	logical, default TRUE. Do mutliQC comparison of STAR alignment between all the samples. Outputted in aligned/LOGS folder. See ?STAR.multiQC
`keep.contaminants`	logical, default FALSE. Create and keep contaminant aligning bam files, default is to only keep unaliged fastq reads, which will be further processed in "ge" genome alignment step. Useful if you want to do further processing on contaminants, like specific coverage of specific tRNAs etc.
`keep.contaminants.type`	logical, default "bam". If aligned files of contaminants are kept, which format to output as, only supports "bam" for now. Fasta / Fastq will be implemented later.
`keep.unaligned.genome`	logical, default FALSE. Create and keep reads that did not align at the genome alignment step, default is to only keep the aliged bam file. Useful if you want to do further processing on plasmids/custom sequences.
`script.folder`	location of STAR index script, default internal ORFik file. You can change it and give your own if you need special alignments.
`script.single`	location of STAR single file alignment script, default internal ORFik file. You can change it and give your own if you need special alignments.

Details

Can only run on unix systems (Linux, Mac and WSL (Windows Subsystem Linux)), and requires a minimum of 30GB memory on genomes like human, rat, zebrafish etc.
If for some reason the internal STAR alignment bash script will not work for you, like if you want more customization of the STAR/fastp arguments. You can copy the internal alignment script, edit it and give that as the script used for this function.
The trimmer used is fastp (the fastest I could find), also works on (Linux, Mac and WSL (Windows Subsystem Linux)). If you want to use your own trimmer set file1/file2 to the location of the trimmed files from your program.
A note on trimming from creator of STAR about trimming: "adapter trimming it definitely needed for short RNA sequencing. For long RNA-seq, I would agree with Devon that in most cases adapter trimming is not advantageous, since, by default, STAR performs local (not end-to-end) alignment, i.e. it auto-trims." So trimming can be skipped for longer reads.

Value

output.dir, can be used as as input in ORFik::create.experiment

Examples

# First specify directories wanted (temp directory here)
config_file <- tempfile()
#config.save(config_file, base.dir = tempdir())
#config <- ORFik::config(config_file)

## Yeast RNA-seq samples (small genome)
#project <- ORFik::config.exper("chalmers_2012", "Saccharomyces_cerevisiae", "RNA-seq", config)
#annotation.dir <- project["ref"]
#fastq.input.dir <- project["fastq RNA-seq"]
#bam.output.dir <- project["bam RNA-seq"]

## Download some SRA data and metadata (subset to 50k reads)
# info <- download.SRA.metadata("SRP012047", outdir = conf["fastq RNA-seq"])
# info <- info[1:2,] # Subset to 2 first libraries
# download.SRA(info, fastq.input.dir, rename = FALSE, subset = 50000)

## No contaminant depletion:
# annotation <- getGenomeAndAnnotation("Saccharomyces cerevisiae", annotation.dir)
# index <- STAR.index(annotation)
# STAR.align.folder(fastq.input.dir, bam.output.dir,
#                   index, paired.end = FALSE) # Trim, then align to genome

## Human Ribo-seq sample (NB! very large genome and libraries!)
## Requires >= 32 GB memory
#project <- ORFik::config.exper("subtelny_2014", "Homo_sapiens", "Ribo-seq", config)
#annotation.dir <- project["ref"]
#fastq.input.dir <- project["fastq Ribo-seq"]
#bam.output.dir <- project["bam Ribo-seq"]

## Download some SRA data and metadata (full libraries)
# info <- download.SRA.metadata("DRR041459", fastq.input.dir)
# download.SRA(info, fastq.input.dir, rename = FALSE)
## Now align 2 different ways, without and with contaminant depletion

## No contaminant depletion:
# annotation <- getGenomeAndAnnotation("Homo sapiens", annotation.dir)
# index <- STAR.index(annotation)
# STAR.align.folder(fastq.input.dir, bam.output.dir,
#                   index, paired.end = FALSE)

## All contaminants merged:
# annotation <- getGenomeAndAnnotation(
#    organism = "Homo_sapiens",
#    phix = TRUE, ncRNA = TRUE, tRNA = TRUE, rRNA = TRUE,
#    output.dir = annotation.dir
#    )
# index <- STAR.index(annotation)
# STAR.align.folder(fastq.input.dir, bam.output.dir,
#                   index, paired.end = FALSE,
#                   steps = "tr-ge")

Roleren/ORFik documentation built on Nov. 13, 2024, 10 p.m.