![]() If we are exhausting the junction information in our library, the line will plateau as the amount of data increases. The number of junctions discovered at each level of downsampling is plotted. , 95% of total alignments from the BAM or SAM file. This module checks for saturation of junction discovery by resampling 5%, 10%, 15%. The pattern we see here at the beginning of the reads may be caused by biases caused by random hexamer priming that arose when making cDNA from RNA ( for further discussion). Read - Nucleotide vs Cycle (Phred base score vs. Go through the generated PDFs by browsing through the following directory in a web browser: Read_quality.py -i Pairend_nonStrandSpecific_36mer_Human_hg19.bam -o tutorial ![]() Checking percent of paired-end reads that are properly paired 5. Examining the total number of reads aligning to each sample 4. Determining the percent uniquely mapping reads 3. Read_NVC.py -i Pairend_nonStrandSpecific_36mer_Human_hg19.bam -o tutorial Checking the total percent of reads aligning to the genome 2. Read_GC.py -i Pairend_nonStrandSpecific_36mer_Human_hg19.bam -o tutorial Read_duplication.py -i Pairend_nonStrandSpecific_36mer_Human_hg19.bam -o tutorial In one of our tutorials we described how to use TopHat mapper. Read_distribution.py -r hg19_UCSC_knownGene.bed -i Pairend_nonStrandSpecific_36mer_Human_hg19.bam HISAT2 is a fast alignment program for mapping next-generation sequencing reads (both DNA and RNA). Junction_saturation.py -r hg19_UCSC_knownGene.bed -i Pairend_nonStrandSpecific_36mer_Human_hg19.bam -o tutorial Many previously described RNA-seq aligners were developed as extensions of contiguous (DNA) short read mappers, which were used to either align short reads to a database of splice junctions or align split-read portions contiguously to a reference genome, or a combination thereof. Junction_annotation.py -r hg19_UCSC_knownGene.bed -i Pairend_nonStrandSpecific_36mer_Human_hg19.bam -o tutorial Inner_distance.py -r hg19_UCSC_knownGene.bed -i Pairend_nonStrandSpecific_36mer_Human_hg19.bam -o tutorial Infer_experiment.py -r hg19_UCSC_knownGene.bed -i Pairend_nonStrandSpecific_36mer_Human_hg19.bam Reads aligned to the transcript models are then re-mapped on to genomic. It uses BWA to align reads to the genome and reference transcript models (including annotated exon-exon junctions) specifically allowing for the possibility of a single read spanning multiple exons. GeneBody_coverage.py -r hg19_UCSC_knownGene.bed -i Pairend_nonStrandSpecific_36mer_Human_hg19.bam -o tutorial JAGuaR is an alignment protocol for RNA-seq reads that uses an extended reference to increase alignment sensitivity. Bam_stat.py -i Pairend_nonStrandSpecific_36mer_Human_hg19.bamĬlipping_profile.py -i Pairend_nonStrandSpecific_36mer_Human_hg19.bam -o tutorial -s "PE"
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