Figure 1. The overall workflow of RNA-seq QC

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Peter Tsai Bioinformatics Institute, University of Auckland

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Figure 1. The overall workflow of RNA-seq QC Figure 1. The overall workflow of RNA-seq QC. (A colour version of this figure is available online at: http://bfg.oxfordjournals.org) From: Multi-perspective quality control of Illumina RNA sequencing data analysis Brief Funct Genomics. 2016;16(4):194-204. doi:10.1093/bfgp/elw035 Brief Funct Genomics | © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com

Figure 2. (A) RNA with good quality, RIN = 10 Figure 2. (A) RNA with good quality, RIN = 10. (B) RNA with feasible quality, RIN = 6.9. (C) RNA with poor quality, RIN = 1.8. (D) Typical small RNA quality, RIN is usually <5 for small RNA. (A colour version of this figure is available online at: http://bfg.oxfordjournals.org) From: Multi-perspective quality control of Illumina RNA sequencing data analysis Brief Funct Genomics. 2016;16(4):194-204. doi:10.1093/bfgp/elw035 Brief Funct Genomics | © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com

Figure 3. This figure was produced from QC3 Figure 3. This figure was produced from QC3. (A) Example of a long RNA-seq sample with expected base quality score. Read 2 tends to have a slightly lower median base score than read 1, but it is not usually a quality concern. (B) Example of a long RNA-seq sample with potential base quality problem, as denoted by the sudden drops of median base quality in read 2 of pair-end read sequencing. (C) Example of a sRNA-seq sample with expected quality score. Owing to trimming, the reads of sRNA-seq are of unequal length, causing the quality dropping more dramatically toward the end of the read cycles. (D) Example of a long RNA-seq sample with expected nucleotide distribution, as denoted by the stable nucleotide distribution across the samples. (E) Example of a long RNA-seq sample with a potential nucleotide distribution issue, as denoted by the unstable distribution across the cycles. (F) Example nucleotide distribution from a sRNA-seq sample (same sample as C). Large variation of nucleotide distribution can be observed which is typical for sRNA-seq data. (A colour version of this figure is available online at: http://bfg.oxfordjournals.org) From: Multi-perspective quality control of Illumina RNA sequencing data analysis Brief Funct Genomics. 2016;16(4):194-204. doi:10.1093/bfgp/elw035 Brief Funct Genomics | © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com

Figure 4. (A) Example of a small RNA sample with potential quality issue based on read length distribution after trimming. The high peak at zero indicates the majority of the reads are adapter sequences. (B) Example of a small RNA sample with expected read length after trimming. We observe a high peak at 22 for miRNA and another at 33 for tRNA. From: Multi-perspective quality control of Illumina RNA sequencing data analysis Brief Funct Genomics. 2016;16(4):194-204. doi:10.1093/bfgp/elw035 Brief Funct Genomics | © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com

Figure 5. (A) Expected insert size distribution from RNA-seq data Figure 5. (A) Expected insert size distribution from RNA-seq data. A peak should be observed between 100 and 200 nucleotides, as it is the targeted fragment size in most RNA-seq kits. The high peak for insert size >1000 nucleotide is caused by improperly mapped pairs. These are usually errors caused by sequencing or alignment, or indicate structural variations. (B) A graphical explanation of the reason of inaccurate insert size estimation in the SAM/BAM file. (A colour version of this figure is available online at: http://bfg.oxfordjournals.org) From: Multi-perspective quality control of Illumina RNA sequencing data analysis Brief Funct Genomics. 2016;16(4):194-204. doi:10.1093/bfgp/elw035 Brief Funct Genomics | © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com

Figure 6. (A and B) Examples of expected read count distribution after variance stabilization normalization, where all samples share similar distributions. (C and D) Examples of uneven read count distribution after normalization. This might indicate batch effect or outlier samples. (A colour version of this figure is available online at: http://bfg.oxfordjournals.org) From: Multi-perspective quality control of Illumina RNA sequencing data analysis Brief Funct Genomics. 2016;16(4):194-204. doi:10.1093/bfgp/elw035 Brief Funct Genomics | © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com

Figure 7. Top miRNAs detected in each sample Figure 7. Top miRNAs detected in each sample. Left: human small RNA data set with high detected abundance of hsa-miR-486-5p. Right: mouse small RNA data set with high detected abundance of mmu-miR-486a-5p. (A colour version of this figure is available online at: http://bfg.oxfordjournals.org) From: Multi-perspective quality control of Illumina RNA sequencing data analysis Brief Funct Genomics. 2016;16(4):194-204. doi:10.1093/bfgp/elw035 Brief Funct Genomics | © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com