TapeStation QC of Bead-Cleaned Samples

SIFP-2025
Author

Kathleen Durkin

Published

July 25, 2025

After cleaning and concentrating all extractions using KAPA Pure Beads, used the TapeStation with a Genomic Tape to check DNA concetration, quality, and size distribution. Followed protocol for Agilent 4200 gDNA.

See full TapeStation summaries here: Tape 1 summary, Tape 2 summary

Oldest age group

Mid-age group

Modern age group

(Note the above plots have different y-axis scales)

Tape # Catalog # Year Collected DIN Conc. (ng/uL) % reads >300bp long
1 14366 1886 - 1.57 63.15
1 19054 1898 1.2 21.6 82.13
1 52295 1912 1.0 19.9 61.63
1 1180630 1912 1.0 4.33 62.25
1 50603 1912 1.0 42.4 73.82
1 51732 1960 2.1 157 93.80
1 51892 1960 1.9 137 94.20
1 14399 1886 1.2 100 86.10
1 42137 1898 1.1 46.3 86.49
1 51730 1960 1.1 59.6 71.13
1 51728 1960 1.4 23.7 89.90
1 100609 2000 2.9 261 96.34
1 100610 2000 1.9 164 93.57
1 1007393 2000 2.8 218 93.49
1 1018355 2002 1.8 115 89.98
2 1606824 2019 1.7 353 90.80
2 1606826 2019 3.4 408 97.21
2 1740336 2019 3.1 368 96.99
2 1740363 2019 3.3 343 97.40
2 1740390 2019 2.4 291 96.83
2 1740407 2019 3.1 260 96.89
2 50368 1880 1.7 68.3 91.68
2 51727 1960 1.3 15.3 87.12
2 51729 1960 1.5 18.7 89.23
2 51857 1960 1.4 109 93.00
2 51858 1960 1.3 24.2 88.78
2 51859 1960 1.5 117 92.46
2 51860 1960 1.4 15.6 86.46
2 51861 1960 1.7 94.1 94.72 
library(googlesheets4)
Warning: package 'googlesheets4' was built under R version 4.2.3
library(tidyr)
Warning: package 'tidyr' was built under R version 4.2.3
library(ggplot2)
Warning: package 'ggplot2' was built under R version 4.2.3
library(plotly)
Warning: package 'plotly' was built under R version 4.2.3

Attaching package: 'plotly'
The following object is masked from 'package:ggplot2':

    last_plot
The following object is masked from 'package:stats':

    filter
The following object is masked from 'package:graphics':

    layout
conc <- read_sheet("https://docs.google.com/spreadsheets/d/1zDMy7AOCM8Ug85q4vr9CoMDxfF-zDJk1DnaxakSt1Hg/edit?usp=sharing")
! Using an auto-discovered, cached token.
  To suppress this message, modify your code or options to clearly consent to
  the use of a cached token.
  See gargle's "Non-interactive auth" vignette for more details:
  <https://gargle.r-lib.org/articles/non-interactive-auth.html>
ℹ The googlesheets4 package is using a cached token for 'kdurkin1@uw.edu'.
✔ Reading from "E_tourneforti".
✔ Range 'E_tourneforti_samples'.
conc$Collection.Age <- ifelse(conc$Year.Collected < 1920, "pre-1920",
                              ifelse(conc$Year.Collected > 1999, "Modern", "1960s-70s"))
conc$Collection.Age <- factor(conc$Collection.Age,
                              levels = c("pre-1920", "1960s-70s", "Modern"),
                              ordered = TRUE)

p1 <- ggplot(conc, aes(x=Tapestation.Perc.300up, 
                       y=Tapestation.Conc.ngul, 
                       color = Collection.Age,
                       text = paste("Catalog #:", Catalog.Number,
                                    "<br>Year:", Year.Collected,
                                    "<br>% >300kb:", Tapestation.Perc.300up,
                                    "<br>Conc:", Tapestation.Conc.ngul))) +
  geom_jitter(size = 2, width = 0.25, height = 0) +
  geom_hline(yintercept = 13.33, color = 'black') +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  xlab("Percent of reads >300kb") +
  ylab("Concentration (ng/uL)")

p2 <- ggplot(conc, aes(x=Tapestation.DIN, 
                       y=Tapestation.Conc.ngul, 
                       color=Collection.Age,
                       text = paste("Catalog #:", Catalog.Number,
                                    "<br>Year:", Year.Collected,
                                    "<br>DIN:", Tapestation.DIN,
                                    "<br>Conc:", Tapestation.Conc.ngul))) +
  geom_jitter(size = 2, width = 0.25, height = 0) +
  geom_hline(yintercept = 13.33, color = 'black') +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  xlab("DNA Integrity Number (DIN)") +
  ylab("Concentration (ng/uL)")

p3 <- ggplot(conc, aes(x=Tapestation.DIN, 
                       y=Tapestation.Perc.300up, 
                       color=Collection.Age,
                       text = paste("Catalog #:", Catalog.Number,
                                    "<br>Year:", Year.Collected,
                                    "<br>DIN:", Tapestation.DIN,
                                    "<br>% >300kb:", Tapestation.Perc.300up))) +
  geom_jitter(size = 2, width = 0.25, height = 0) +
  geom_hline(yintercept = 13.33, color = 'black') +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  xlab("DNA Integrity Number (DIN)") +
  ylab("Percent of reads >300kb")

ggplotly(p1, tooltip = "text")
ggplotly(p2, tooltip = "text")
ggplotly(p3, tooltip = "text")

Sample selection

Based on the above results, I can pick which samples are most suited for sequencing, considering DNA concentration (as a proxy for total DNA available) and DNA integrity. I’m not sure whether % >300bp reads or DIN is the most appropriate measure of DNA integrity here, but they’re highly correlated. I want n=5 for each of my three collection periods.

Modern (<25 yo)
USNM 1606826
USNM 1740336
USNM 1740363
USNM 100609
USNM 1740407

Mid-age (1960s-1970s):
USNM 51732
USNM 51892
USNM 51861
USNM 51857
USNM 51859

Historic (>100 yo):
USNM 50368
USNM 14399
USNM 42137
USNM 19054
USNM 50603

selected <- c("USNM 1606826", "USNM 1740336", "USNM 1740363", "USNM 100609", "USNM 1740407", "USNM 51732", "USNM 51892", "USNM 51861", "USNM 51857", "USNM 51859", "USNM 50368", "USNM 14399", "USNM 42137", "USNM 19054", "USNM 50603")

conc$Selection <- ifelse(conc$Catalog.Number %in% selected, "yes", "no")

p21 <- ggplot(conc, aes(x=Tapestation.Perc.300up, 
                       y=Tapestation.Conc.ngul, 
                       color = Collection.Age,
                       shape=Selection,
                       text = paste("Catalog #:", Catalog.Number,
                                    "<br>Year:", Year.Collected,
                                    "<br>% >300kb:", Tapestation.Perc.300up,
                                    "<br>Conc:", Tapestation.Conc.ngul))) +
  geom_jitter(size = 2, width = 0.25, height = 0) +
  geom_hline(yintercept = 13.33, color = 'black') +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  xlab("Percent of reads >300kb") +
  ylab("Concentration (ng/uL)")

p22 <- ggplot(conc, aes(x=Tapestation.DIN, 
                       y=Tapestation.Conc.ngul, 
                       color=Collection.Age,
                       shape=Selection,
                       text = paste("Catalog #:", Catalog.Number,
                                    "<br>Year:", Year.Collected,
                                    "<br>DIN:", Tapestation.DIN,
                                    "<br>Conc:", Tapestation.Conc.ngul))) +
  geom_jitter(size = 2, width = 0.25, height = 0) +
  geom_hline(yintercept = 13.33, color = 'black') +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  xlab("DNA Integrity Number (DIN)") +
  ylab("Concentration (ng/uL)")

ggplotly(p21, tooltip = "text")
ggplotly(p22, tooltip = "text")