Affordable short read sequencing

If there is one thing that has gotten cheaper in this economy, it would be short read next generation sequencing. During my grad research, I did a few bulk RNA-seq and ChIP-seq experiments and I remember holding my breath before seeing each quote in the mid four figures¹. In recent years, Illumina’s NovaSeq has made this type of sequencing much more affordable². See the plot below comparing cost (in CAD) per million reads between NextSeq550 and NovaSeq X Plus.

Code

df <- data.frame(
  Machine = c("NextSeq500","NextSeq500", "NovaSeqX"),
  DollarsPerMillion = c(20.96, 9.01, 1.51),
  Flow = c("Mid","High", "10B")
)

df$Flow <- factor(df$Flow, levels = c("Mid","High", "10B"))

library(ggplot2)

ggplot(df, aes(x = Flow, y = DollarsPerMillion, fill = Machine)) +
  geom_bar(stat = "identity", position = position_dodge()) +
  geom_text(aes(label = DollarsPerMillion),
          position = position_dodge(width = 0.9),
          vjust = -0.5,
          size = 5) +
  labs(
    y = "$ per 1M reads (CAD)",
    x = "Flow Cell",
    title = ""
  ) +
  scale_fill_manual(
    values= c("NextSeq500" = "grey75",
              "NovaSeqX" = "blue3")
  )+
  ylim(0,23)+
  theme_bw()+ # gets rid of grey background in the plot area
  theme(
      # grid lines can be controversial, in my field it's not seen often
      panel.grid.minor=element_blank(), 
      panel.grid.major=element_blank(),
      axis.text = element_text(color="black",size=14), # default font size is too small
      axis.ticks = element_line(colour="black"), # just plain old black please
      axis.title = element_text(size=14), # default font size is too small
      legend.title=element_text(size=14, vjust=0.5, hjust=0.5),
      legend.text=element_text(size=14, vjust=0.5, hjust=0),
      plot.margin=unit(c(1,1,1,1),"cm"),
      plot.background=element_rect(fill="transparent", colour=NA),
      panel.border = element_rect(colour="black"), # I like to draw a crisp border
      panel.background=element_rect(fill="transparent", colour=NA),
      legend.background=element_rect(fill="transparent", colour=NA),
      legend.box.background=element_rect(fill="transparent", colour=NA),
      legend.key=element_rect(fill="transparent", colour=NA),
      legend.position = 'right',
      aspect.ratio=1)

These prices are from two genomics cores in Ontario. Cycle numbers are 150, 75, and 100 bp in total, respectively. This is not meant to be a comprehensive breakdown of every Illumina machine/flow cell vs. cost, cycle, etc, but the general cost advantage is clear. This could also help with the back-of-the-envelope-math while budgeting; nowadays you can get a million reads for buck five.

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Footnotes

Those numbers included library prep kit cost, too, so they ran a bit higher.↩︎
Assuming you already bought one for $1.2M, or have access to it through a local genomics core. The latter being the more likely scenario.↩︎