1 Introduction

The Bioinformatics Hub generated this report includes stats and diagnostic plots of the Oxford Nanopore Sequencing data.

An analytical platform designed to quantify and compare Nanopore sequencing data quality across multiple experimental conditions, enabling detection of batch-specific variability and ensuring consistency in sequencing performance.

2 Prerequisite

2.1 Environment

OS: macOS 26.7.1
Platform: aarch64-apple-darwin20
Software: R (v4.5.1)
Pakcages: dplyr (v1.1.4), ggplot2 (v4.0.1), ggrepel (v0.9.6), ggridges (v0.5.7), ggsankey (v0.0.99999), stringr (v1.6.0), reshape2 (v1.4.5), RColorBrewer (v1.1-3), viridis (v0.6.5), cowplot (v1.2.0), data.table (v1.18.0)

2.2 Metadata

Names used in this report (runs are named Name-01 to Name-06):
- Microbial: ZymoBIOMICS Microbial Community Standard (D6300)
- Gut: ZymoBIOMICS Gut Microbiome Standard (D6331)
- Oral: ZymoBIOMICS Oral Microbiome Standard (D6332)

2.3 Run report

MinKNOW automatically generates a run report containing summary statistics, configuration details, output metrics, and alignment information to help users evaluate sequencing data quality.
Source: https://nanoporetech.com/document/experiment-companion-minknow#run-report
Reports (one per run; failed: MinKNOW did not write a report):

Data	Run 01	Run 02	Run 03	Run 04	Run 05	Run 06
Microbial	Microbial-01	Microbial-02	Microbial-03	failed	Microbial-05	Microbial-06
Gut	Gut-01	Gut-02	Gut-03	Gut-04	Gut-05	Gut-06
Oral	Oral-01	Oral-02	Oral-03	Oral-04	Oral-05	Oral-06

3 Quality assessment

3.1 Preparation

Set working directory

# Do not run
baseDir <- "WORKING_DIRECTORY" # where the Nanopore stats files are located
setwd(baseDir)
outDir <- file.path(baseDir, "Results")

# Load libraries
library(dplyr)
library(ggplot2)
library(ggrepel)
library(ggridges)
library(ggsankey)
library(stringr)
library(reshape2)
library(RColorBrewer)
library(viridis)
library(cowplot)

3.2 Sequence summary

Sequencing output summary files were generated using Dorado with the summary command.
Dorado basecalled without a quality-score filter (--min-qscore not set), so the summaries hold every read, barcoded or not.
18 runs, one MinKNOW run each: Microbial-01 to -06 and Gut-01 to -06 (Flongle, one flow cell per run, SQK-RBK114-24) and Oral-01 to -06 (six runs on one flow cell, FBG46173, SQK-RBK114-96).
Pore activity files could not be regenerated when MinKNOW failed and only the available files were included.

3.2.1 Import data

tsvFiles <- list.files(path = file.path(baseDir, "Data"), pattern = "tsv", full.names = TRUE)
# remove
# remove
# remove
if (file.exists(file.path(outDir, "summaryL.RDS"))) { summaryL <- readRDS(file.path(outDir, "summaryL.RDS")); summary <- readRDS(file.path(outDir, "summary.RDS")) } else { # remove
# remove
# remove
# remove
# sequencing summary
summaryL <- lapply(seq_along(tsvFiles), function(idx) {
        tsvFile <- tsvFiles[idx]
        tsv <- as.data.frame(data.table::fread(tsvFile, sep = "\t", header = TRUE))
        tsv$sample <- gsub(".tsv.gz", "", basename(tsvFile))
        return(tsv)
})
names(summaryL) <- gsub(".tsv.gz", "", basename(tsvFiles))
summaryL <- summaryL[sampleNames] # reorder

summary <- do.call(rbind, summaryL)
summary$sample <- factor(summary$sample, levels = sampleNames)
# remove
# remove
# remove
saveRDS(summaryL, file.path(outDir, "summaryL.RDS")); saveRDS(summary, file.path(outDir, "summary.RDS")) }  # remove
# remove
# remove
# remove
# pore activity
csvFiles <- list.files(path = file.path(baseDir, "Data"), pattern = "csv", full.names = TRUE)

poreL <- lapply(seq_along(csvFiles), function(idx) {
        csvFile <- csvFiles[idx]
        csv <- read.csv(csvFile, header = TRUE)
        csv$sample <- gsub(".csv", "", basename(csvFile))
        return(csv)
})
names(poreL) <- gsub(".csv", "", basename(csvFiles))
poreRun <- list() # one MinKNOW run per plate

basename(tsvFiles)

##  [1] "Gut-01.tsv.gz"       "Gut-02.tsv.gz"       "Gut-03.tsv.gz"      
##  [4] "Gut-04.tsv.gz"       "Gut-05.tsv.gz"       "Gut-06.tsv.gz"      
##  [7] "Microbial-01.tsv.gz" "Microbial-02.tsv.gz" "Microbial-03.tsv.gz"
## [10] "Microbial-04.tsv.gz" "Microbial-05.tsv.gz" "Microbial-06.tsv.gz"
## [13] "Oral-01.tsv.gz"      "Oral-02.tsv.gz"      "Oral-03.tsv.gz"     
## [16] "Oral-04.tsv.gz"      "Oral-05.tsv.gz"      "Oral-06.tsv.gz"

3.2.2 Sequence output

Legend:

readLengthMax <- max(summary$sequence_length_template)
readQualityMax <- max(summary$mean_qscore_template)

for (sampleName in sampleNames) {
        stat <- summaryL[[sampleName]]
        readDf <- data.frame(
                readLength = stat$sequence_length_template,
                readQuality = stat$mean_qscore_template
        )
        readDf <- densityColors(df = readDf, cols = heatCols)

        cat("#### ", sampleName, "\n")
        cat("\n")
        cat("  * Number of reads: ", format(nrow(readDf), big.mark=",",scientific = FALSE), "\n")
        plot(log10(readDf$readLength), readDf$readQuality, col = readDf$Col, xlab = "Length, log10", ylab = "Quality, phred", pch = 20, xlim = c(0, log10(readLengthMax)), ylim = c(0, readQualityMax))
        cat("\n\n")
}

3.2.2.1 Microbial-01

Number of reads: 3,563

3.2.2.2 Microbial-02

Number of reads: 976

3.2.2.3 Microbial-03

Number of reads: 8,373

3.2.2.4 Microbial-04

Number of reads: 11,410

3.2.2.5 Microbial-05

Number of reads: 1,511

3.2.2.6 Microbial-06

Number of reads: 68,036

3.2.2.7 Gut-01

Number of reads: 12,199

3.2.2.8 Gut-02

Number of reads: 4,338

3.2.2.9 Gut-03

Number of reads: 4,468

3.2.2.10 Gut-04

Number of reads: 10,402

3.2.2.11 Gut-05

Number of reads: 36,657

3.2.2.12 Gut-06

Number of reads: 29,725

3.2.2.13 Oral-01

Number of reads: 300,749

3.2.2.14 Oral-02

Number of reads: 239,432

3.2.2.15 Oral-03

Number of reads: 199,813

3.2.2.16 Oral-04

Number of reads: 172,524

3.2.2.17 Oral-05

Number of reads: 61,054

3.2.2.18 Oral-06

Number of reads: 79,751

3.2.3 Read quality

Read lengths were log10-transformed according to the formula log10(read length + 1)

	Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
Microbial-01	1.00	4.30	10.94	11.38	17.03	39.60
Microbial-02	1.04	11.05	16.75	15.85	20.91	34.34
Microbial-03	1.00	8.32	14.38	13.65	18.60	41.41
Microbial-04	1.00	6.31	12.86	12.67	18.32	41.52
Microbial-05	1.00	2.18	3.80	7.82	13.64	43.44
Microbial-06	1.00	12.90	16.56	15.79	19.21	41.74
Gut-01	1.00	8.79	14.01	13.27	17.40	37.64
Gut-02	1.00	8.86	13.81	13.09	17.40	40.47
Gut-03	1.00	5.45	11.79	11.74	16.99	36.50
Gut-04	1.00	5.15	13.06	11.99	17.57	37.59
Gut-05	1.00	13.49	16.97	16.19	19.75	42.49
Gut-06	1.00	8.71	14.36	13.27	17.70	41.36
Oral-01	1.00	16.10	21.16	19.86	24.64	47.80
Oral-02	1.00	14.24	19.82	18.73	23.89	48.41
Oral-03	1.00	13.04	18.94	18.04	23.48	48.36
Oral-04	1.00	13.23	19.12	18.22	23.53	47.02
Oral-05	1.00	15.48	20.39	19.49	24.19	44.27
Oral-06	1.00	13.99	19.61	18.61	23.75	47.83

# remove
# remove
# remove
if (file.exists(file.path(outDir, "ridgeQuality.RDS"))) { ridgeQuality <- readRDS(file.path(outDir, "ridgeQuality.RDS")) } else { # remove
# remove
# remove
# remove
ridgeQuality <- ggplot(summary, aes(x = mean_qscore_template, y = factor(sample, levels = rev(sampleNames)), fill = ..x..)) + # first run on top, as in the table
        geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
        scale_fill_viridis(name = "Quality, phred", option = "C") +
        labs(
                x = "Quality, phred", 
                y = "Run"
        ) +
        theme_bw() +
        theme(
                axis.line = element_line(colour = "black"),
                panel.grid.minor = element_blank(),
                panel.border = element_blank(),
                panel.background = element_blank(),
                axis.text.x = element_text(angle = 0, vjust = 0, hjust = 0.5),
                text = element_text(size = 12)
        ) +
        geom_vline(xintercept = 20, linetype = "dashed", color = "red", size = 1)
# remove
# remove
# remove
saveRDS(ridgeQuality, file.path(outDir, "ridgeQuality.RDS")) } # remove
# remove
# remove
# remove
print(ridgeQuality)

3.2.4 Read length

	Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
Microbial-01	5	65.0	125.0	1,291.88	302.50	322,891
Microbial-02	6	80.5	207.5	1,015.41	775.75	41,436
Microbial-03	6	98.0	217.0	917.40	596.00	1,178,993
Microbial-04	5	95.0	158.0	878.09	774.00	31,146
Microbial-05	7	112.0	206.0	2,964.43	430.50	732,180
Microbial-06	5	245.0	621.0	1,358.66	1,614.00	249,115
Gut-01	5	108.0	295.0	1,141.28	956.50	558,609
Gut-02	5	101.0	239.0	923.43	619.50	268,741
Gut-03	5	58.0	118.0	1,370.32	316.25	659,941
Gut-04	5	117.0	284.0	1,134.10	1,042.00	494,907
Gut-05	5	242.0	654.0	1,402.95	1,626.00	893,358
Gut-06	5	119.0	464.0	2,009.43	1,592.00	1,035,677
Oral-01	5	328.0	864.0	1,823.66	2,196.00	587,479
Oral-02	5	164.0	535.0	1,456.93	1,412.00	772,292
Oral-03	5	125.0	325.0	1,057.69	900.00	719,738
Oral-04	5	140.0	382.0	1,065.24	953.00	540,453
Oral-05	5	213.0	579.0	1,318.93	1,420.00	300,924
Oral-06	5	193.0	489.0	1,194.06	1,233.00	317,137

# remove
# remove
# remove
if (file.exists(file.path(outDir, "ridgeLength.RDS"))) { ridgeLength <- readRDS(file.path(outDir, "ridgeLength.RDS")) } else { # remove
# remove
# remove
# remove
ridgeLength <- ggplot(summary, aes(x = log10(sequence_length_template + 1), y = factor(sample, levels = rev(sampleNames)), fill = ..x..)) + # first run on top, as in the table
        geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
        scale_fill_viridis(name = "Length, log10") +
        labs(
                x = "Length, log10", 
                y = "Run"
        ) +
        theme_bw() +
        theme(
                axis.line = element_line(colour = "black"),
                panel.grid.minor = element_blank(),
                panel.border = element_blank(),
                panel.background = element_blank(),
                axis.text.x = element_text(angle = 0, vjust = 0, hjust = 0.5),
                text = element_text(size = 12)
        )
# remove
# remove
# remove
saveRDS(ridgeLength, file.path(outDir, "ridgeLength.RDS")) } # remove
# remove
# remove
# remove
print(ridgeLength)

3.2.5 Pore activity

for (sampleName in sampleNames) {
        if (sampleName %in% names(poreL) & sampleName %in% names(summaryL)) {
                summDf <- summaryL[[sampleName]]
                if (sampleName %in% names(poreRun)) summDf <- summDf[startsWith(summDf$run_id, poreRun[[sampleName]]), ] # the run the pore activity file belongs to
                binHours <- if (max(summDf$start_time) / 3600 > 10) 1 else 0.5 # 1-h bins for runs longer than 10 h, 30-min bins otherwise
                secBreaks <- seq(-1, (max(summDf$start_time) + binHours * 3600), binHours * 3600)
                summDf$TimeInGroup <- cut(summDf$start_time, breaks = secBreaks, labels = as.character(seq(binHours, (length(secBreaks) - 1) * binHours, binHours)))
                tsvDf <- data.frame(
                        TimeInGroup = summDf$TimeInGroup,
                        Value = summDf$mean_qscore_template / 50 * 100,
                        Category = "Quality"
                )

                poreDf <- poreL[[sampleName]]
                poreDf$Active <- "No"
                poreDf$Active[which(poreDf$Channel.State %in% c("strand", "adapter"))] <- "Yes"
                poreAct <- poreDf %>% group_by(Experiment.Time..minutes.) %>%  summarise(Total = sum(State.Time..samples.), Live = sum(State.Time..samples.[Active == "Yes"]))
                minBreaks <- seq(-1, (max(poreAct$Experiment.Time..minutes.) + binHours * 60), binHours * 60) # same bins as the read quality
                poreAct$TimeInGroup <- cut(poreAct$Experiment.Time..minutes., breaks = minBreaks, labels = as.character(seq(binHours, (length(minBreaks) - 1) * binHours, binHours)))
                poreAct <- poreAct %>% mutate(Prop = Live / Total)
                csvDf <- data.frame(
                        TimeInGroup = poreAct$TimeInGroup,
                        Value = poreAct$Prop * 100,
                        Category = "Active pores"
                )
                
                poreActivityPlot <- poreActivity(df = rbind(csvDf, tsvDf), cols = c("#1B9E77", "#D95F02"))
                
                cat("#### ", sampleName, "\n")
                cat("\n")
                cat("  * Read quality and pore activity over time.\n")
                print(poreActivityPlot)
                cat("\n\n")
        } else {
                summDf <- summaryL[[sampleName]]
                binHours <- if (max(summDf$start_time) / 3600 > 10) 1 else 0.5 # 1-h bins for runs longer than 10 h, 30-min bins otherwise
                secBreaks <- seq(-1, (max(summDf$start_time) + binHours * 3600), binHours * 3600)
                timeLabels <- as.character(seq(binHours, (length(secBreaks) - 1) * binHours, binHours))
                summDf$TimeInGroup <- cut(summDf$start_time, breaks = secBreaks, labels = timeLabels)
                tsvDf <- data.frame(
                        TimeInGroup = summDf$TimeInGroup,
                        Value = summDf$mean_qscore_template / 50 * 100,
                        Category = "Quality"
                )
                csvDf <- data.frame( # dummy data for consistent visualization
                        TimeInGroup = timeLabels,
                        Value = rep(0, length(timeLabels)),
                        Category = "Active pores"
                )
                poreActivityPlot <- poreActivity(df = rbind(csvDf, tsvDf), cols = c("#1B9E77", "#D95F02"))

                cat("#### ", sampleName, "\n")
                cat("\n")
                cat("  * _MinKNOW_ failed to produce a pore activity report.\n")
                print(poreActivityPlot)
                cat("\n\n")
        }
}

3.2.5.1 Microbial-01

Read quality and pore activity over time.

3.2.5.2 Microbial-02

Read quality and pore activity over time.

3.2.5.3 Microbial-03

Read quality and pore activity over time.

3.2.5.4 Microbial-04

MinKNOW failed to produce a pore activity report.

3.2.5.5 Microbial-05

Read quality and pore activity over time.

3.2.5.6 Microbial-06

Read quality and pore activity over time.

3.2.5.7 Gut-01

Read quality and pore activity over time.

3.2.5.8 Gut-02

Read quality and pore activity over time.

3.2.5.9 Gut-03

Read quality and pore activity over time.

3.2.5.10 Gut-04

Read quality and pore activity over time.

3.2.5.11 Gut-05

Read quality and pore activity over time.

3.2.5.12 Gut-06

Read quality and pore activity over time.

3.2.5.13 Oral-01

Read quality and pore activity over time.

3.2.5.14 Oral-02

Read quality and pore activity over time.

3.2.5.15 Oral-03

Read quality and pore activity over time.

3.2.5.16 Oral-04

Read quality and pore activity over time.

3.2.5.17 Oral-05

Read quality and pore activity over time.

3.2.5.18 Oral-06

Read quality and pore activity over time.

3.3 Barcode-level summary

Barcode-level statistics are from NanoPlot on each barcode’s FASTQ (all reads of the run’s one sample barcode, with no quality-score filter).
Reads that were not assigned to a barcode (unclassified) are not included, so the number of reads shown in the Figures below is lower than that presented in the Figures above.

3.3.1 Import data

data <- read.table(file.path(baseDir, "Data", "NanoStats_summary.txt"), header = T, stringsAsFactors = F, sep = "\t")
data$Plate <- sapply(str_split(data$Plate_Barcode, "_"), "[[", 1)
data$Plate <- factor(data$Plate, levels = sampleNames)
data$Barcode <- sapply(str_split(data$Plate_Barcode, "_"), "[[", 2)
data <- data[order(data$Plate), ] # run order as everywhere else in the report
head(data[,c("Plate", "Barcode", "number_of_reads", "n50")])

##           Plate   Barcode number_of_reads  n50
## 7  Microbial-01 barcode21            1385 3730
## 8  Microbial-02 barcode23             656 4242
## 9  Microbial-03 barcode01            5195 1876
## 10 Microbial-04 barcode03            6222 3437
## 11 Microbial-05 barcode05             541 1812
## 12 Microbial-06 barcode09           58109 3043

3.3.2 Per-run statistics

One sample barcode per run, so each statistic is a single value per run.

statDf <- data.frame(
        Run = data$Plate,
        Barcode = data$Barcode,
        Reads = format(data$number_of_reads, big.mark = ","),
        Bases = format(data$number_of_bases, big.mark = ","),
        N50 = format(data$n50, big.mark = ","),
        `Length, median` = format(data$median_read_length, big.mark = ","),
        `Length, mean` = format(data$mean_read_length, big.mark = ",", nsmall = 1),
        `Quality, median` = data$median_qual,
        `Quality, mean` = data$mean_qual,
        check.names = FALSE
)
kable(statDf, align = c("l", "l", rep("r", 7)), row.names = FALSE)

Run	Barcode	Reads	Bases	N50	Length, median	Length, mean	Quality, median	Quality, mean
Microbial-01	barcode21	1,385	1,121,836	3,730	139.0	810.0	16.5	12.8
Microbial-02	barcode23	656	796,257	4,242	254.0	1,213.8	18.0	13.5
Microbial-03	barcode01	5,195	3,957,279	1,876	310.0	761.7	16.4	12.9
Microbial-04	barcode03	6,222	8,011,921	3,437	444.5	1,287.7	16.8	12.9
Microbial-05	barcode05	541	353,362	1,812	223.0	653.2	15.0	11.3
Microbial-06	barcode09	58,109	82,384,087	3,043	672.0	1,417.8	16.7	14.2
Gut-01	barcode15	8,245	10,060,717	3,117	438.0	1,220.2	15.0	12.1
Gut-02	barcode17	2,624	2,107,273	1,846	336.0	803.1	15.3	13.0
Gut-03	barcode19	1,683	1,184,875	2,245	213.0	704.0	15.8	12.5
Gut-04	barcode07	5,353	8,395,868	3,549	716.0	1,568.4	16.5	13.9
Gut-05	barcode11	30,108	44,944,736	3,006	775.0	1,492.8	17.3	15.0
Gut-06	barcode13	18,862	33,689,213	3,749	913.0	1,786.1	16.0	13.6
Oral-01	barcode05	263,381	486,835,086	3,887	933.0	1,848.4	21.3	16.5
Oral-02	barcode07	198,084	292,715,856	3,724	635.0	1,477.7	20.6	15.9
Oral-03	barcode09	172,739	178,084,835	3,183	356.0	1,030.9	19.8	15.1
Oral-04	barcode11	147,603	155,960,335	2,945	419.0	1,056.6	20.0	15.4
Oral-05	barcode13	53,383	73,214,440	3,121	644.0	1,371.5	20.8	16.7
Oral-06	barcode15	69,130	84,264,042	2,957	536.0	1,218.9	20.2	15.3

3.3.3 Read quality

qualDf <- data.frame(
        Plate = data$Plate,
        Barcode = data$Barcode,
        Lt10 = data$number_of_reads - data$Reads..Q10._count,
        Gt10Lt15 = data$Reads..Q10._count - data$Reads..Q15._count,
        Gt15lt20 = data$Reads..Q15._count - data$Reads..Q20._count,
        Gt20lt25 = data$Reads..Q20._count - data$Reads..Q25._count,
        Gt25lt30 = data$Reads..Q25._count - data$Reads..Q30._count,
        Gt30 = data$Reads..Q30._count
)
qDf <- melt(qualDf, value.name = "Count")
colnames(qDf) <- c("Plate", "Barcode", "Quality", "Count")
qDf$Quality <- factor(qDf$Quality, levels = c("Gt30", "Gt25lt30", "Gt20lt25", "Gt15lt20", "Gt10Lt15", "Lt10"), labels = c(">=30", ">=25 and <30", ">=20 and <25", ">=15 and <20", ">=10 and <15", "<10"))

ggplot(qDf, aes(x = Plate, y = Count, fill = Quality)) + 
        geom_bar(position="fill", stat="identity") +
        labs(
                x = "Run", 
                y = "Proportion"
        ) +
        theme_bw() +
        theme(
                axis.line = element_line(colour = "black"),
                panel.grid.minor = element_blank(),
                panel.border = element_blank(),
                panel.background = element_blank(),
                axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
                text = element_text(size = 12)
        ) + scale_fill_grey()

4 SessionInfo

si <- sessioninfo::session_info(pkgs = "attached")
si$platform$date <- NULL                                  # no timestamps on public pages
si$platform$tz <- NULL
si$platform$pandoc <- NULL
si$platform$quarto <- NULL
si

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value
##  version  R version 4.5.1 (2025-06-13)
##  os       macOS Tahoe 26.7.1
##  system   aarch64, darwin20
##  ui       X11
##  language (EN)
##  collate  en_CA.UTF-8
##  ctype    en_CA.UTF-8
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package      * version   date (UTC) lib source
##  cowplot      * 1.2.0     2025-07-07 [1] CRAN (R 4.5.0)
##  dplyr        * 1.1.4     2023-11-17 [1] CRAN (R 4.5.0)
##  ggplot2      * 4.0.1     2025-11-14 [1] CRAN (R 4.5.2)
##  ggrepel      * 0.9.6     2024-09-07 [1] CRAN (R 4.5.0)
##  ggridges     * 0.5.7     2025-08-27 [1] CRAN (R 4.5.0)
##  ggsankey     * 0.0.99999 2025-11-04 [1] Github (davidsjoberg/ggsankey@b675d0d)
##  knitr        * 1.51      2025-12-20 [1] CRAN (R 4.5.2)
##  RColorBrewer * 1.1-3     2022-04-03 [1] CRAN (R 4.5.0)
##  reshape2     * 1.4.5     2025-11-12 [1] CRAN (R 4.5.0)
##  sessioninfo  * 1.2.3     2025-02-05 [1] CRAN (R 4.5.0)
##  stringr      * 1.6.0     2025-11-04 [1] CRAN (R 4.5.0)
##  viridis      * 0.6.5     2024-01-29 [1] CRAN (R 4.5.0)
##  viridisLite  * 0.4.2     2023-05-02 [1] CRAN (R 4.5.0)
##  yaml         * 2.3.12    2025-12-10 [1] CRAN (R 4.5.2)
## 
##  [1] /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library
##  * ── Packages attached to the search path.
## 
## ──────────────────────────────────────────────────────────────────────────────

Dorado Run Analyzer (Doradora)

Heewon Seo