count features from ngs reads -凯发k8网页登录
this example shows how to count features from paired-end sequencing reads after aligning them to the whole human genome curated by the genome reference consortium. this example uses genome reference consortium human build 38 patch release 12 (grch38.p12) as the human genome reference.
prerequisites and data set
this example works on the unix® and mac platforms only. download the bioinformatics toolbox™ interface for bowtie aligner support package from the add-on explorer. for details, see .
this example assumes you have:
downloaded and extracted the refseq assembly from .
downloaded and organized some paired-end reads data. this example uses the exome sequencing data from the . paired-end reads are indicated by '_1' and '_2' in the filenames.
build index
construct an index for aligning reads to the reference using bowtie2build. the file gcf_000001405.38_grch38.p12_genomic.fna contains the human reference genome in the fasta format. bowtieidx is the base name of the reference index files. the '--threads 8' option specifies the number of parallel threads to build index files faster. you do not need to specify full file paths for *.fna or *.index files if you are running the example from the same folder location. specify the full paths if you wish to store the files elsewhere or run the example from a different folder.
bowtieidx = 'gcf_000001405.38_grch38.p12_genomic.index'; buildflag = bowtie2build('gcf_000001405.38_grch38.p12_genomic.fna',... bowtieidx,'--threads 8');
align reads to reference
align paired-end reads to the reference using bowtie2. you can create a bowtie2alignoptions object to specify different options, such as the number of parallel threads to use.
opt = bowtie2alignoptions; opt.numthreads = 8; reads1 = 'hg00096_1.fastq'; reads2 = 'hg00096_2.fastq'; bowtie2(bowtieidx,reads1,reads2,'hg00096.sam',opt);
selectively align to gene of interest
sam files can be very large. use biomap to select only the data for the correct reference. for this example, consider apoe, which is a gene on chromosome 19 linked to alzheimer's disease. create a smaller bam file for apoe to improve performance.
apoeref = 'nc_000019.10'; % reference name for chromosome 19 in hg38 bm = biomap('hg00096.sam','selectreference',apoeref); write(bm, 'hg00096.bam','format','bam');
warning: found invalid tag in header type: 'pg'. ignoring tag 'pn:bowtie2'. warning: the read sequences in input sam file do not appear to be ordered according to the start position of their alignments with the reference sequence. because of this, there will be a decrease in performance when accessing the reads. for maximum performance, order the read sequences in the sam file, before creating a biomap object.
summarize read counts
use featurecount to compare the number of transcripts for each apoe variant using a gtf file. a full table of features is included in the grch38.p12 assembly in gff format, which can be converted to gtf using cuffgffread. this example uses a simplified gtf based on apoe transcripts. apoe_gene.gtf is included with the software.
[feattable, summary] = featurecount('apoe_gene.gtf','hg00096.bam',... 'metafeature','transcript_id');
processing gtf file apoe_gene.gtf ... processing bam file hg00096.bam ... processing reference nc_000019.10 ... 10000 reads processed ... 20000 reads processed ... 30000 reads processed ... 40000 reads processed ... 50000 reads processed ... 60000 reads processed ... 70000 reads processed ... 80000 reads processed ... 90000 reads processed ... 100000 reads processed ... 110000 reads processed ... 120000 reads processed ... 130000 reads processed ... 140000 reads processed ... 150000 reads processed ... 160000 reads processed ... 170000 reads processed ... 180000 reads processed ... 190000 reads processed ... 200000 reads processed ... 210000 reads processed ... 220000 reads processed ... 230000 reads processed ... 240000 reads processed ... 250000 reads processed ... 260000 reads processed ... 270000 reads processed ... 280000 reads processed ... 290000 reads processed ... 300000 reads processed ... 310000 reads processed ... 320000 reads processed ... 330000 reads processed ... 340000 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see also
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