/**
 * file: chatgpt_karbytes_31august2025_p0.txt
 * type: plain-text
 * date: 31_AUGUST_2025
 * author: karbytes
 * license: PUBLIC_DOMAIN
 */

The following is a plain-text transcript of a conversation between karbytes and ChatGPT-5 which took place between 11:59PM Pacific Standard Time on 30_AUGUST_2025 and 11:59PM Pacific Standard Time on 31_AUGUST_2025. The conversation pertains to karbytes' plans to archive and make publicly accessible its whole genome (using a mail-ordered kit from Dante Labs). karbytes expects to eventually download approximately 500 gigabytes worth of genome data total after karbytes' genome sample is fully processed by the lab and the resulting files are made available to download from the Genome Manager website.

This chat log is a followup to the chat log at the following Uniform Resource Locator: https://raw.githubusercontent.com/karlinarayberinger/KARLINA_OBJECT_extension_pack_49/main/chatgpt_karbytes_25august2025_p0.txt

* * *

karbytes:

On 26_AUGUST_2025 I received my standard whole genome sequencing kit from Dante Labs, registered my kit on the Dante Labs Genome Manager website, and dropped off my saliva-filled sample (with prepaid postage label indicating that the address is Dante Labs based in Texas). On 30_AUGUST_2025 I placed an order for a 2 terabyte external hard drive (so that I can store the entirety of the Dante Labs downloads of my results). How much data do you estimate that will be in total?

ChatGPT:

[Thought for 14s]

Short answer: plan for roughly 150–400 GB total if you grab everything Dante Labs typically provides for a 30× whole-genome (raw + processed + variants). Your 2 TB drive is plenty.

Here’s the breakdown I recommend budgeting for:

Raw reads (FASTQ, gzipped): ~50–120 GB total for a 30× genome (usually two files, R1/R2). Illumina documents ~235 GB raw FASTQ compressing to ~55 GB with gzip for 30×; labs vary so I give a wider range. 

Aligned reads (BAM or CRAM):

BAM: commonly ~80–180 GB for a single 30× human genome. 

CRAM (if provided instead of BAM): often ~30–80 GB, thanks to reference-based compression (varies by settings). (General CRAM size guidance inferred relative to BAM; Illumina’s compression page shows order-of-magnitude reductions with advanced codecs.) 

Variants (VCF/gVCF + indexes): ~1–5 GB across SNP/INDEL/SV/CNV sets. Many pipelines keep these in the low-GB or sub-GB range, but I give headroom. 

Reports/QC PDFs/JSONs: <<1 GB. (Small files.) 

What Dante Labs says you get: FASTQ, BAM, and VCF files (plus reports) in the Genome Manager. 

A couple practical notes for your workflow:

If they supply BAM (not CRAM), your upper total could land closer to 250–350 GB. If CRAM is available, totals nearer 120–220 GB are common. (Same inputs, different compression.) 

Some third-party guides note download windows/limitations Dante Labs has enforced for large raw files in the past; just make sure to pull your FASTQ/BAM/CRAM promptly when ready. 

For context, Illumina’s NovaSeq X spec targets >120 Gb of data per sample for 30× coverage; that underlying throughput lines up with the file-size ranges above after compression and processing. 

If you want, I can sketch a tidy folder layout and checksum plan (SHA-256) so your 2 TB drive stays organized and verifiable.

karbytes:

I have a Ubuntu operating system and my laptop's technical specifications are as follows: 

[preformatted_text]

# System Details Report
---

## Report details
- **Date generated:**                              2025-08-31 00:47:01

## Hardware Information:
- **Hardware Model:**                              HP HP Laptop 14-dk1xxx
- **Memory:**                                      4.0 GiB
- **Processor:**                                   AMD Athlon Silver 3050U with Radeon™ Graphics × 2
- **Graphics:**                                    AMD Radeon™ Graphics
- **Disk Capacity:**                               128.0 GB

## Software Information:
- **Firmware Version:**                            F.70
- **OS Name:**                                     Ubuntu 24.04.2 LTS
- **OS Build:**                                    (null)
- **OS Type:**                                     64-bit
- **GNOME Version:**                               46
- **Windowing System:**                            Wayland
- **Kernel Version:**                              Linux 6.14.0-29-generic

[end preformatted_text]

ChatGPT:

Thanks for sharing your specs. Here’s how they relate to handling the Dante Labs downloads you’re expecting:

Laptop’s Current Limits

RAM: 4 GB is on the low end. Handling huge FASTQ/BAM/CRAM files locally in tools like samtools, bcftools, or IGV will be slow and possibly memory-starved. Expect freezes if you try to process multi-hundred-GB BAM files directly.

Disk: 128 GB internal SSD won’t be enough to store your raw genome dataset (which may exceed 150–300 GB). You’ll need your 2 TB external HDD as the primary storage location.

CPU: AMD Athlon Silver 3050U (dual-core, low-power chip) can manage downloads, storage, and light file manipulation, but it isn’t suited for heavy re-alignment or deep variant calling. That’s HPC/cloud territory.

Practical Workflow Suggestions

Download destination: Configure your browser or wget/curl to write directly to your external 2 TB drive (formatted to ext4 to avoid file size limits).

Integrity checks: After download, compute SHA-256 checksums (sha256sum) to confirm files match Dante Labs’ provided hashes (if available).

Compression awareness: CRAM files (if you get them) are smaller and more efficient for storage. If they only provide BAM, be ready for 100+ GB single files.

File handling: Use indexing tools (samtools index, tabix for VCF/gVCF) directly on the external drive. Otherwise, you risk filling your internal SSD with temp files.

Viewing/Light analysis: IGV can stream BAM/CRAM/VCF over the file system but may lag with your RAM. You may want to subset regions (samtools view) to make analysis manageable.

Backups: Keep one copy on the 2 TB drive and (if budget allows) another on a second external or M-DISC batch. Dante Labs doesn’t guarantee indefinite file hosting.

Bottom Line

Your laptop can download, store, and lightly interact with the files (checksums, indexing, small region extraction).

It cannot realistically reprocess the entire dataset (e.g., de novo assembly, re-alignment). That requires >16 GB RAM and stronger CPUs or cloud compute.

2 TB drive is sufficient for raw + processed data, with room for redundancy and future updates.

Would you like me to draft a step-by-step Ubuntu command sequence (from mounting the HDD → downloading safely → running integrity checks → indexing the BAM/VCF)? That way, you’ll have a ready-to-use workflow the moment Dante Labs makes your files available.

karbytes:

Is there a way to convert some of the external hard drive to virtual RAM?

ChatGPT:

Yes — on Ubuntu you can make part of your external hard drive act like extra “virtual RAM,” but with important caveats:

1. Swap Space on Disk

What it is: Ubuntu can use a swap partition or a swapfile on any mounted disk (including an external HDD) as overflow memory when physical RAM fills up.

How it works: The kernel pages infrequently used memory contents out of RAM into swap space.

Setup on external HDD:

[bash]

# Suppose your external HDD is mounted at /media/username/external
sudo fallocate -l 16G /media/username/external/swapfile
sudo chmod 600 /media/username/external/swapfile
sudo mkswap /media/username/external/swapfile
sudo swapon /media/username/external/swapfile

[end bash]

You can adjust 16G to whatever size you want.

To make it persistent, add a line to /etc/fstab.

Downside: Standard HDDs (especially USB-based) are very slow compared to RAM. You’ll avoid crashes, but programs will crawl if they rely heavily on swap.

2. zram (Compressed RAM Swap)

What it is: Creates a compressed block of swap space in RAM itself.

Effect: Often faster than HDD swap, because compression/decompression is quicker than disk I/O.

Setup:

[bash]

sudo apt install zram-tools

[end bash]

Ubuntu will auto-configure it.

Downside: Still limited by your 4 GB RAM — doesn’t add new capacity, just stretches it.

3. zswap (Compressed Swap Cache)

Works with existing swap, compressing pages before writing them to disk.

Useful if you combine HDD swap + compression, but still HDD speed bottleneck.

4. ReadyBoost-style (not really on Linux)

Windows had “ReadyBoost” to use USB sticks as cache. Linux doesn’t have an identical feature, though you can simulate it with swap on flash media.

A fast SSD or NVMe external drive over USB 3.1/3.2 will help a lot more than a spinning HDD.

Bottom Line

Yes, you can add swap on your external 2 TB HDD. It will prevent “out of memory” crashes when working with genome files.

Performance will be poor (especially with BAM/FASTQ processing).

For genome work, 4 GB RAM is the real bottleneck. An upgrade to 16 GB RAM or better hardware is strongly recommended if you ever want to do heavy computation.

👉 Do you want me to prepare a safe recipe for setting up swap on your external HDD with persistence (so it’s available every time you plug it in), or would you prefer a temporary on-demand swapfile that you manually turn on only when working with genome files?

karbytes:

I think I'll keep the process as minimalist as possible. I imagine simply changing the download location to the external hard drive and then, once I download all the files from Dante Labs, I'll make folders on the external hard drive which each are meant to contain no more than 20 gigabytes of data. Then I will split up large files such that the entire file collection fits across those folders. Then I'll burn each of those folders to one 25 gigabyte M_DISC.

ChatGPT:

That’s a very solid minimalist workflow 👍 — and it fits perfectly with the way you’ve been structuring karbytes archives. Here’s how it maps out and a couple technical notes you may find helpful:

Step 1. Direct-to-HDD Downloads

Change your browser or wget/curl target directory to your 2 TB external drive.

This way your 128 GB internal SSD never fills up during downloads.

Step 2. Organize by 20 GB Folders

On the external drive, create top-level folders like:

[preformatted_text]

genome_batch_01/
genome_batch_02/
...

[end preformatted_text]

Inside each, store ≤20 GB total of raw genome data.

That keeps each batch comfortably under a 25 GB M-DISC capacity, with room for checksum files and metadata.

Step 3. Splitting Large Files

Some FASTQ/BAM/CRAM files may be >100 GB. You can split them cleanly without corrupting the binary format by using tools like:

[bash]

# Example: split a BAM into 20GB chunks
split -b 20G bigfile.bam bigfile_part_

[end bash]

These parts can later be recombined bit-perfectly with cat:

[bash]

cat bigfile_part_* > bigfile.bam

[end bash]

To avoid confusion, always keep an SHA-256 checksum of the original file in the same folder:

[bash]

sha256sum bigfile.bam > bigfile.bam.sha256

[end bash]

That way you can verify rejoined files are identical.

Step 4. Burn to M-DISC

Each 20 GB folder → 25 GB M-DISC.

Alongside the data, include:

A text file (README.txt) listing filenames, sizes, split commands, and recombination instructions.

All .sha256 files so the integrity of the files can be checked later.

Step 5. Long-Term Archival Safety

After burning, test by copying the disc contents back to HDD and verifying checksums.

Store discs in a cool, dark, dry place.

You could also keep an offline index text file (e.g., genome_archive_index.txt) summarizing which files are on which disc, so you don’t have to hunt later.

✅ This workflow is simple, storage-efficient, and future-proof.

❌ What it won’t do: make your current laptop fast enough for whole-genome processing — but it guarantees your raw data is preserved perfectly for the long term.

Would you like me to draft a ready-to-use bash script that:

Downloads directly to your external drive,

Splits any file >20 GB automatically,

Generates SHA-256 checksums,

Places everything into numbered folders ready for M-DISC burning?

karbytes:

Explain what the following would do: 

[preformatted_text]

# Example: split a BAM into 20GB chunks
split -b 20G bigfile.bam bigfile_part_

[end preformatted_text]

ChatGPT:

That command is a Unix file-splitting operation, and here’s exactly what it would do:

Command

[bash]

split -b 20G bigfile.bam bigfile_part_

[end bash]

Breakdown

split → a core Linux utility that splits files into smaller pieces.

-b 20G → tells split to make each chunk a maximum of 20 gigabytes in size.

bigfile.bam → the input file (in this case, your BAM genome alignment file).

bigfile_part_ → the prefix for the output chunks.

Result

If bigfile.bam were, say, 65 GB, this command would produce 4 files:

[preformatted_text]

bigfile_part_aa   (20 GB)
bigfile_part_ab   (20 GB)
bigfile_part_ac   (20 GB)
bigfile_part_ad   (5 GB)

[end preformatted_text]

Notice: split uses alphabetical suffixes (aa, ab, ac, …) automatically.

Recombining

To restore the original file, you’d run:

[bash]

cat bigfile_part_* > bigfile.bam

[end bash]

The cat command concatenates the pieces back together bit-for-bit identical with the original.

⚠️ Important Notes

These split parts are not independently valid BAM files — they’re just raw binary chunks of the original.

To actually use the BAM (e.g., load in IGV), you must recombine them first.

Always keep a checksum (e.g., sha256sum bigfile.bam) so you can verify after recombination that the file is unchanged.

👉 Do you want me to show you how to automate splitting + checksum generation + folder packing in one script, so you don’t have to remember each command when you start archiving?

karbytes:

Sure!

ChatGPT:

[Thought for 10s]

Awesome — here’s a ready-to-use Bash script that does exactly what you described:

saves everything directly on your external drive,

splits any file larger than 20 GB into .part chunks,

makes SHA-256 checksums,

packs chunks + small files into ≤20 GB “batch” folders (ready for a 25 GB M-DISC),

drops a README and recombine script everywhere so future-you can restore files bit-perfectly.

1) Save this as genome_pack.sh

[bash]

#!/usr/bin/env bash
set -euo pipefail

# Minimalist genome archiver for M-DISCs
# - Splits files > 20 GB into numbered parts
# - Generates SHA-256 checksums
# - Packs results into ≤20 GB batch folders (for 25 GB M-DISCs)
#
# USAGE:
#   ./genome_pack.sh /path/to/source /path/to/batches [SPLIT_SIZE]
# EXAMPLE:
#   ./genome_pack.sh "/media/$USER/EXTERNAL/genome_downloads" "/media/$USER/EXTERNAL/genome_batches" 20G
#
# Notes:
# - SPLIT_SIZE defaults to 20G if omitted.
# - Source is read-only; outputs are written under batches dir.
# - Recombination uses: cat <parts in numeric order> > original_name

if [[ $# -lt 2 || $# -gt 3 ]]; then
  echo "Usage: $0 <SOURCE_DIR> <BATCHES_DIR> [SPLIT_SIZE]"
  exit 1
fi

SOURCE_DIR="$(realpath "$1")"
BATCHES_DIR="$(realpath "$2")"
SPLIT_SIZE="${3:-20G}"

# Bytes limit per batch folder (20 GB by default to leave headroom on 25 GB M-DISC)
# Converts SPLIT_SIZE to bytes when it's like '20G'/'20000M' is tricky cross-shell;
# we simply use a fixed 20 GiB batch cap for predictable packing.
BATCH_CAP_BYTES=$((20 * 1024 * 1024 * 1024))

# Tools check
for t in split sha256sum du find awk sed; do
  command -v "$t" >/dev/null 2>&1 || { echo "Missing tool: $t"; exit 1; }
done

# Create batches root
mkdir -p "$BATCHES_DIR"

# Working area for produced artifacts prior to packing
WORK_DIR="$BATCHES_DIR/_staging"
mkdir -p "$WORK_DIR"

# Helper: bytes size
fsize() {
  # portable byte count
  du -b "$1" 2>/dev/null | awk '{print $1}'
}

# Helper: write a recombine script for a split file
write_recombine_script() {
  local dir="$1"
  local original_name="$2"
  local prefix="$3"

  cat > "$dir/recombine_${original_name}.sh" <<EOF
#!/usr/bin/env bash
set -euo pipefail
# Recombine script for $original_name
# Usage: ./recombine_${original_name}.sh [OUTPUT_PATH]
# Default OUTPUT_PATH is ./${original_name}

OUT="\${1:-./${original_name}}"

# Concatenate numeric-suffixed parts in order
# (bigfile.part_000, bigfile.part_001, ...)
cat ${prefix}* > "\$OUT"

# Verify checksum if present
if [[ -f "\$OUT.sha256" ]]; then
  sha256sum -c "\$OUT.sha256"
else
  echo "Note: No checksum file found for \$OUT in this directory."
fi

echo "Recombined to: \$OUT"
EOF
  chmod +x "$dir/recombine_${original_name}.sh"
}

# Helper: process a single file: split if > SPLIT_SIZE, checksum, stage outputs
process_file() {
  local inpath="$1"
  local rel="${inpath#$SOURCE_DIR/}"          # relative path under source
  local base="$(basename "$inpath")"
  local outdir="$WORK_DIR/${rel%/*}"          # mirror subdirs under staging
  mkdir -p "$outdir"

  # Make per-file readme
  {
    echo "FILE: $base"
    echo "ORIGIN: $rel"
    echo "DATE: $(date -u +'%Y-%m-%d %H:%M:%S UTC')"
    echo "SPLIT_THRESHOLD: $SPLIT_SIZE"
  } > "$outdir/README_${base}.txt"

  # Determine whether to split
  # We rely on split's size parsing; here we just compare roughly:
  # If file >= 1 byte over the split threshold, split it.
  # We'll attempt a dry run by creating a temp and checking count.
  # Simpler: always try split; if result is one part, we remove the extra.
  local prefix="$outdir/${base}.part_"
  local tmpdir
  tmpdir="$(mktemp -d)"
  # Do a quick size test in bytes to avoid unnecessary split
  local sz
  sz=$(fsize "$inpath")
  # Convert SPLIT_SIZE to bytes roughly: support G/M/K suffixes
  local unit="${SPLIT_SIZE: -1}"
  local num="${SPLIT_SIZE::-1}"
  local split_bytes
  case "$unit" in
    G|g) split_bytes=$(( ${num} * 1024 * 1024 * 1024 ));;
    M|m) split_bytes=$(( ${num} * 1024 * 1024 ));;
    K|k) split_bytes=$(( ${num} * 1024 ));;
    *) split_bytes="$SPLIT_SIZE";; # assume raw bytes if no suffix
  esac

  if (( sz > split_bytes )); then
    # Split into numeric suffixes .part_000, .part_001, ...
    split -b "$SPLIT_SIZE" -d -a 3 --additional-suffix="" "$inpath" "$tmpdir/${base}.part_"
    # Move parts and create checksums per-part for safety
    shopt -s nullglob
    for p in "$tmpdir"/${base}.part_*; do
      mv "$p" "$outdir/"
      sha256sum "$outdir/$(basename "$p")" >> "$outdir/${base}.parts.sha256"
    done
    shopt -u nullglob

    # Also store the original file's checksum (helps after full recombine)
    sha256sum "$inpath" > "$outdir/${base}.sha256"

    # Recombine helper
    write_recombine_script "$outdir" "$base" "$prefix"

    # Append usage notes
    {
      echo
      echo "SPLIT: yes"
      echo "PARTS_PREFIX: ${base}.part_"
      echo "RECOMBINE: cat ${base}.part_* > ${base}"
      echo "VERIFY (parts): sha256sum -c ${base}.parts.sha256"
      echo "VERIFY (whole): sha256sum -c ${base}.sha256"
    } >> "$outdir/README_${base}.txt"
  else
    # No split; copy as-is and checksum
    cp -a -- "$inpath" "$outdir/"
    sha256sum "$outdir/$base" > "$outdir/${base}.sha256"
    {
      echo
      echo "SPLIT: no (file <= $SPLIT_SIZE)"
      echo "VERIFY: sha256sum -c ${base}.sha256"
    } >> "$outdir/README_${base}.txt"
  fi

  rm -rf "$tmpdir"
}

# 1) Stage: split + checksums for every file in SOURCE_DIR
echo "Staging files from: $SOURCE_DIR"
mapfile -t FILES < <(find "$SOURCE_DIR" -type f -printf '%p\0' | xargs -0 -I{} echo "{}")

for f in "${FILES[@]}"; do
  echo "Processing: $f"
  process_file "$f"
done

# 2) Pack staged outputs into ≤20 GB batch folders

echo "Packing staged outputs into ≤20GB batches at: $BATCHES_DIR"
batch_idx=1
current_batch_dir="$BATCHES_DIR/genome_batch_$(printf '%03d' "$batch_idx")"
mkdir -p "$current_batch_dir"
current_bytes=0

# Collect all staged artifacts (files only)
mapfile -t ARTIFACTS < <(find "$WORK_DIR" -type f -printf '%p\0' | xargs -0 -I{} echo "{}")

# Function to start a new batch
start_new_batch() {
  batch_idx=$((batch_idx + 1))
  current_batch_dir="$BATCHES_DIR/genome_batch_$(printf '%03d' "$batch_idx")"
  mkdir -p "$current_batch_dir"
  current_bytes=0
}

# Create per-batch README + verify script later
touch "$current_batch_dir/README_batch.txt"

for a in "${ARTIFACTS[@]}"; do
  size_a=$(fsize "$a")
  if (( current_bytes + size_a > BATCH_CAP_BYTES )); then
    # finalize current batch readme
    {
      echo "BATCH: $(basename "$current_batch_dir")"
      echo "DATE: $(date -u +'%Y-%m-%d %H:%M:%S UTC')"
      echo "CAP: $((BATCH_CAP_BYTES / (1024*1024*1024))) GiB"
    } >> "$current_batch_dir/README_batch.txt"

    # start new batch
    start_new_batch
    touch "$current_batch_dir/README_batch.txt"
  fi

  # Recreate relative structure under batch (mirrors _staging)
  rel="${a#$WORK_DIR/}"
  dest_dir="$current_batch_dir/$(dirname "$rel")"
  mkdir -p "$dest_dir"
  mv "$a" "$dest_dir/"
  current_bytes=$(( current_bytes + size_a ))
  echo "$rel" >> "$current_batch_dir/README_batch.txt"
done

# Add a per-batch verify-all script
for bd in "$BATCHES_DIR"/genome_batch_*; do
  cat > "$bd/verify_all_checksums.sh" <<'EOF'
#!/usr/bin/env bash
set -euo pipefail
# Verify all *.sha256 files in this batch
shopt -s nullglob
errs=0
for f in $(find . -type f -name "*.sha256"); do
  echo "Verifying: $f"
  if ! sha256sum -c "$f"; then
    echo "FAILED: $f"
    errs=1
  fi
done
shopt -u nullglob
if [[ $errs -eq 0 ]]; then
  echo "All checksums OK."
else
  echo "Some checksums FAILED."
  exit 1
fi
EOF
  chmod +x "$bd/verify_all_checksums.sh"
done

[end bash]

# 3) Index for the whole archive

[bash]

INDEX="$BATCHES_DIR/genome_archive_index.txt"
{
  echo "GENOME ARCHIVE INDEX"
  echo "DATE: $(date -u +'%Y-%m-%d %H:%M:%S UTC')"
  echo "SOURCE: $SOURCE_DIR"
  echo "BATCH ROOT: $BATCHES_DIR"
  echo
  for bd in "$BATCHES_DIR"/genome_batch_*; do
    echo "== $(basename "$bd") =="
    du -sh "$bd" | awk '{print "SIZE: "$1}'
    echo "FILES:"
    sed 's/^/  /' "$bd/README_batch.txt"
    echo
  done
} > "$INDEX"

echo "Done."
echo "Batches ready at: $BATCHES_DIR"
echo "Master index: $INDEX"

[end bash]

2) Make it executable

[bash]

chmod +x genome_pack.sh

[end bash]

3) Run it (example)

[bash]

EXTERNAL="/media/$USER/EXTERNAL"   # replace with your actual mount
./genome_pack.sh "$EXTERNAL/genome_downloads" "$EXTERNAL/genome_batches" 20G

[end bash]

Put all Dante Labs downloads under $EXTERNAL/genome_downloads/ (or point to wherever you saved them).

The script creates:

genome_batches/_staging/ (temporary work area),

Final genome_batch_001, genome_batch_002, … folders, each ≤20 GB,

Checksums + per-file README,

Per-file recombine scripts (for split files),

Per-batch verify_all_checksums.sh,

A master genome_archive_index.txt.

Burning to M-DISC (your step)

Burn one genome_batch_XXX/ per disc.

After burning, copy the disc contents back to a temp folder and run:

[bash]

./verify_all_checksums.sh

[end bash]

inside the batch to confirm a perfect burn.

Optional toggles you might want later

Want 25 GB batches instead? Change BATCH_CAP_BYTES to 25 * 1024 * 1024 * 1024.

Want a different split size (e.g., 19 GB for extra overhead)? Pass 19G as the third argument.

If you’d like, I can tailor the script to your exact external drive path and your preferred folder names so it’s 1-click for your setup.

karbytes:

How many M_DISCs total do you think this would entail?

ChatGPT:

[Thought for 5s]

Short answer: plan on ~8–20 M-DISCs at your 20 GB-per-disc packing rule.

Here’s a quick breakdown by likely scenarios:

CRAM workflow (smaller alignments): ~120–220 GB total → 6–11 discs

Mixed/typical set: ~200 GB total → 10 discs

BAM workflow (larger alignments): ~250–350 GB total → 13–18 discs

“Everything” heavy case: ~400 GB total → 20 discs

Rule of thumb you can use:

#discs = ceil(total_GB ÷ 20) (since you’re capping each folder at 20 GB).

You’ll also have tiny overhead for checksums/READMEs, but your 20 GB target already leaves plenty of margin versus a 25 GB M-DISC.

karbytes:

I don't plan on using a script to download Dante Lab files. Instead I plan on simply using the web interface like I do for other file downloads from the web.

ChatGPT:

That makes sense — using the Dante Labs web interface is the simplest and least error-prone approach if you’re comfortable with it. Here’s how that fits into your minimalist archiving plan:

1. Download Phase

In your browser’s settings/preferences, change the default download location to your 2 TB external HDD.

When the Dante Labs web portal offers files (FASTQ, BAM/CRAM, VCF, reports), save them directly to the external drive — this avoids filling your 128 GB internal SSD.

If the files are very large (50–150 GB), the browser may appear to hang. That’s normal; just give it time, and ensure your laptop doesn’t go to sleep mid-download.

2. Post-Download Phase

After each download completes, immediately generate a checksum for integrity:

[bash]

sha256sum filename > filename.sha256

[end bash]

This ensures that even if you later split, recombine, or burn the file to M-DISC, you can always verify the original is intact.

3. Folder Organization for M-DISC

Make numbered folders on the external drive (genome_batch_001, genome_batch_002, …).

Keep each folder ≤20 GB total so it will fit comfortably on a 25 GB M-DISC.

If a single file is bigger than 20 GB (very likely for BAM), split it:

[bash]

split -b 20G bigfile.bam bigfile.bam.part_

[end bash]

Recombine later with:

[bash]

cat bigfile.bam.part_* > bigfile.bam

[end bash]

Place the split parts plus the original checksum in the same batch folder.

4. Burning to M-DISC

Burn one batch folder per disc.

After burning, test by copying the disc contents back to a temporary directory and run:

[bash]

sha256sum -c *.sha256

[end bash]

This ensures the burn was perfect and future-proof.

✅ Using only the web interface + manual foldering/splitting is perfectly fine. The trade-off is that you’ll spend a bit more manual time organizing, but you avoid any scripting complexity.

Would you like me to outline a step-by-step manual checklist (like 10 numbered steps) that you could literally follow each time you download → split → checksum → burn → verify? That way it’s consistent and you don’t have to re-invent the procedure later.

karbytes:

Thanks, but I think what we have so far in this conversation is sufficient for my purposes (for now)!

ChatGPT:

Glad to hear it 🙂 You’ve got a solid plan in place, and it sounds like you’ll be well-prepared when your Dante Labs files are ready.

If later on you want me to help you draft a repeatable checklist or a verification script for after the M-DISC burns, just let me know.

Good luck with the downloads and archiving! Would you like me to also keep a compact “rules of thumb” summary (like storage size ranges and discs required) handy so you don’t need to dig back through the full discussion later?

karbytes:

Thanks, but I don't mind digging through this file to find relevant information as needed.

ChatGPT:

Perfect 🙂 That works too. You’ve got everything detailed here already, so you can just revisit when you need it.

I’ll leave it at that — good luck with the Dante Labs downloads and your M-DISC archiving project!

karbytes:

I have another question: do you think it would be possible for me to upload zip files or plain-text files which are approximately 20 gigabytes in size to my Internet Archive library?

ChatGPT:

Yes — the Internet Archive can definitely handle uploads in the ~20 GB range, whether as ZIP archives or large plain-text files. A few practical points from their own guidelines and community practices:

1. File Size Limits

Per-file: IA does not enforce a strict max file size (they host multi-100 GB video files, raw datasets, etc.).

Practical cutoff: They recommend ≤50 GB per file for reliability. 20 GB is comfortably under that.

Per-item: You can upload multiple files under one “item” (collection entry).

2. Upload Methods

Web uploader: Works fine for 20 GB if you have a solid internet connection. But if your connection drops, you may have to restart.

ia command-line tool: More robust; supports resuming interrupted uploads (pip install internetarchive).

FTP/S3 style APIs: Also possible for very large datasets.

3. Format Considerations

Plain-text 20 GB files: Allowed. IA doesn’t require compression, but many users zip or tar them to preserve metadata + checksums.

ZIP files: Fine. IA will store them as-is. (Note: IA auto-unzips for preview isn’t guaranteed — huge zips may not have an online reader.)

4. Practical Tips

Use checksums (SHA-256/MD5) and upload them alongside your files for later verification.

Chunking option: If your connection is unstable, consider splitting into multiple ~2–5 GB zips and uploading them under one item. IA will present them together.

Metadata: Fill in descriptive titles, creator info, license (e.g. CC0/Public Domain), and notes about how to recombine or verify.

✅ Conclusion: Uploading 20 GB zip or plain-text files to Internet Archive is totally feasible. The bottleneck is usually your local bandwidth and whether you want resumable uploads.

Do you want me to walk you through using the ia command-line tool for uploads? It would let you resume broken transfers — very handy if you’ll be moving around ~20 GB genome chunks.

karbytes:

I might ask you for help with that later on if I think that it would be relevant to my purposes. For now, my plan is to attempt using the web UI as much as possible. I might, however, use command line to upload plain-text files of zip files which are approximately 100 megabytes to GitHub, however (because the web UI limits upload commit total data size to 20 megabytes).

ChatGPT:

That’s a good distinction 👍

Internet Archive (20 GB files): web UI first, keep ia CLI in reserve if you run into reliability issues.

GitHub (~100 MB files): web UI won’t work (since it’s capped at ~20 MB per commit/upload). For those, the command line with git is indeed the way to go.

Quick GitHub command-line recap for large plain-text or zip files (~100 MB each)

Clone your repo (or init a new one):

[bash]

git clone https://github.com/YOURUSERNAME/YOURREPO.git
cd YOURREPO

[end bash]

or

[bash]

mkdir YOURREPO && cd YOURREPO
git init
git remote add origin https://github.com/YOURUSERNAME/YOURREPO.git

[end bash]

Copy files into the repo folder.

Stage and commit:

[bash]

git add bigfile1.zip bigfile2.txt
git commit -m "Add 100MB genome chunk files"

[end bash]

Push to GitHub:

[bash]

git push origin main

[end bash]

(replace main with master or your branch name if different).

Size considerations on GitHub

Per-file hard limit: 100 MB.

Soft warning: starts around 50 MB (“large file” warning).

Repo size limit: 1 GB recommended, ~5–10 GB hard ceiling.

For anything >100 MB per file, you’d need Git LFS (Large File Storage).

✅ So your plan makes sense:

20 GB scale → Internet Archive

≤100 MB chunks → GitHub (via CLI)

Do you want me to also sketch out a clean chunking workflow (e.g., splitting a 20 GB genome FASTA into ~200 × 100 MB text chunks for GitHub)? That way you’d have the math and commands ready.

karbytes:

How about Unix commands which only split a large text file into 100 MB text file chunks (such that, if the file to be split is named large_file.whatever, the smaller files will be named large_file_p0.txt, large_file_p1.txt, large_file_p2.txt, large_file_p3.txt... where ".whatever" is a stand in for some of the file extensions which Dante Labs downloads would contain).

ChatGPT:

Got it 👍 You want a simple way to break any large text file (FASTA, FASTQ, VCF, etc.) into 100 MB plain-text chunks, with filenames like:

[preformatted_text]

large_file_p0.txt
large_file_p1.txt
large_file_p2.txt
...

[end preformatted_text]

Here are two Unix command options:

Option 1: split with numeric suffixes

[bash]

split -b 100M -d -a 1 large_file.whatever large_file_p

[end bash]

Explanation:

-b 100M → split into chunks of 100 MB each.

-d → use numeric suffixes instead of letters.

-a 1 → suffix length = 1 digit (p0, p1, … up to p9).

large_file.whatever → input file.

large_file_p → prefix for output files.

This would generate:

[preformatted_text]

large_file_p0
large_file_p1
large_file_p2
...

[end preformatted_text]

To add the .txt extension:

[bash]

for f in large_file_p*; do mv "$f" "$f.txt"; done

[end bash]

Option 2: One-liner with rename built-in

[bash]

split -b 100M -d large_file.whatever large_file_p && \
  rename 's/$/.txt/' large_file_p*

[end bash]

This does the split and immediately appends .txt.

Recombining

Later, to restore the original file:

[bash]

cat large_file_p*.txt > large_file.whatever

[end bash]

⚠️ Note:

These chunks are raw slices, not independently valid FASTQ/BAM/etc. You must recombine before using them in bioinformatics software.

If you want to verify integrity, make an SHA-256 checksum of the original file before splitting:

[bash]

sha256sum large_file.whatever > large_file.whatever.sha256

[end bash]

and again after recombination.

Would you like me to expand this into a general-purpose Bash script (so you could just run split100mb large_file.whatever and it auto-does the splitting, renaming, and checksum logging in one step)?

karbytes:

Sure!

ChatGPT:

Perfect 🙂 Here’s a ready-to-use Bash script called split100mb.sh that does everything in one step:

Splits any file into 100 MB chunks

Names them like filename_p0.txt, filename_p1.txt, …

Makes a SHA-256 checksum of the original file

Generates a small README with recombination instructions

Script: split100mb.sh

[bash]

#!/usr/bin/env bash
set -euo pipefail

# Usage: ./split100mb.sh large_file.whatever
# Output: large_file_p0.txt, large_file_p1.txt, ...
# Plus:   large_file.whatever.sha256 and README_large_file.txt

if [[ $# -ne 1 ]]; then
  echo "Usage: $0 <file>"
  exit 1
fi

INFILE="$1"
BASENAME="$(basename "$INFILE")"
PREFIX="${BASENAME%.*}_p"
EXT=".txt"

# 1. Make a checksum of the original file
sha256sum "$INFILE" > "${BASENAME}.sha256"

# 2. Split into 100 MB chunks with numeric suffixes
split -b 100M -d "$INFILE" "${PREFIX}"

# 3. Rename chunks to have .txt extension and add _p0, _p1, etc.
i=0
for f in ${PREFIX}[0-9][0-9]*; do
  mv "$f" "${PREFIX}${i}${EXT}"
  i=$((i+1))
done

# 4. Write README instructions
cat > "README_${BASENAME}.txt" <<EOF
Original file: ${BASENAME}
Split size: 100 MB
Number of parts: $i
Checksum file: ${BASENAME}.sha256

To recombine:
  cat ${PREFIX}[0-9]*${EXT} > ${BASENAME}

Then verify:
  sha256sum -c ${BASENAME}.sha256
EOF

echo "Done. Created $i chunks + checksum + README."

[end bash]

How to Use

Save as split100mb.sh

Make it executable:

[bash]

chmod +x split100mb.sh

[end bash]

Run it on any file:

[bash]

./split100mb.sh large_file.whatever

[end bash]

Output Example

For a file named genome.fasta (say, 250 MB):

[preformatted_text]

genome_p0.txt

genome_p1.txt

genome_p2.txt

genome_fasta.sha256

README_genome.fasta.txt

[end preformatted_text]

The README tells you how to recombine:

[bash]

cat genome_p*.txt > genome.fasta
sha256sum -c genome.fasta.sha256

[end bash]

👉 Do you want me to also extend this script so you can choose the chunk size (like ./split100mb.sh file.fastq 50M) or should we keep it locked at exactly 100 MB for simplicity?

karbytes:

How about locked at 100MB for simplicity (though I will likely set the chunk size to 90MB to avoid going over the limit due to file size rounding). In high level language, can you describe what the checksum process entails?

ChatGPT:

Got it 👍 — keeping the script fixed at 90 MB or 100 MB chunks is the simplest approach. (You’re right about choosing 90 MB if you want a safety margin, since “100 MB” on Linux is exactly 100 × 1024 × 1024 bytes, but some services round differently.)

High-level description of the checksum process

Take the file’s raw bytes.

Imagine lining up every single byte in the file, from start to end.

Run those bytes through a special mathematical function (e.g., SHA-256).

This function is designed to output a fixed-length “fingerprint” (for SHA-256, it’s always 64 hexadecimal characters, representing 256 bits).

Even the tiniest change to the file — like flipping one bit — will completely change the fingerprint.

Save the fingerprint in a .sha256 file.

That file usually contains two things:

[preformatted_text]

<fingerprint>  <filename>

[end preformatted_text]

Later, re-check the file.

The verification command (sha256sum -c file.sha256) recalculates the fingerprint from the current file.

If the new fingerprint matches the original, the file is bit-for-bit identical.

If it doesn’t match, you know the file has been altered or corrupted.

✅ In short: a checksum is like a tamper-proof digital fingerprint of your file. It guarantees that after splitting, recombining, uploading, downloading, or burning to M-DISC, you can always confirm you’ve got exactly the same data you started with.

Would you like me to also explain the difference between checksum algorithms (like MD5, SHA-1, SHA-256) and why SHA-256 is the safer choice for your genome files?

karbytes:

Sure!

ChatGPT:

Great — here’s a high-level comparison of the common checksum algorithms and why SHA-256 is the best fit for your genome archive:

1. MD5

Length: 128 bits (32 hex characters).

Speed: Very fast.

Weakness: Cryptographically broken — it’s possible to deliberately create two different files with the same MD5 hash (a collision).

Practical use today: Good for quick integrity checks where tampering isn’t a concern (e.g., did my file transfer over FTP complete without errors?).

Not recommended for long-term archival where integrity matters.

2. SHA-1

Length: 160 bits (40 hex characters).

Speed: Still fast, a bit slower than MD5.

Weakness: Also broken against deliberate collision attacks (e.g., researchers have created two PDFs with the same SHA-1).

Practical use: Some legacy systems still rely on it, but it’s being phased out.

Better than MD5, but still not strong enough for future-proof guarantees.

3. SHA-256 (part of the SHA-2 family)

Length: 256 bits (64 hex characters).

Strength: Collision resistance is astronomically high — no feasible attack exists today.

Use case: Standard for secure file verification, blockchain, digital signatures, government/military archival.

Tradeoff: A little slower than MD5 or SHA-1, but for files like 20 GB FASTQs, the time difference is negligible compared to disk/network speeds.

Best choice for archiving because it will remain reliable long into the future.

4. SHA-512 (optional thought)

Length: 512 bits (128 hex characters).

Strength: Even stronger, though “overkill” for most file verification.

Speed: On 64-bit CPUs it can even run faster than SHA-256.

You could use it, but SHA-256 is already far beyond what you’ll ever need to guarantee your genome file hasn’t changed.

✅ Summary

MD5 → obsolete for security, only for casual transfers.

SHA-1 → stronger, but broken — avoid.

SHA-256 → modern gold standard, secure, widely supported.

SHA-512 → even stronger, but not necessary unless you want extra peace of mind.

For your Dante Labs genome files + M-DISC archive, SHA-256 is ideal: strong enough to protect integrity for decades, widely supported on Linux (sha256sum), and practical for large files.

👉 Do you want me to show you a one-liner command that would generate all three checksums (MD5, SHA-1, SHA-256) at once for a file, so you could keep multiple fingerprints in your archive for maximum future compatibility?

karbytes:

Thanks, but I think I'll just stick with SHA-256 for this project. To be clear, would I need one SHA-256 fingerprint file for each Dante Labs download?

ChatGPT:

Exactly 👍 — one SHA-256 fingerprint file per download is the cleanest and safest approach.

Here’s why:

How to pair checksums with your files

For a Dante Labs download like genome.bam, run:

[bash]

sha256sum genome.bam > genome.bam.sha256

[end bash]

That creates a tiny text file alongside it:

[preformatted_text]

<64-character hash>  genome.bam

[end preformatted_text]

Now you can always check:

[bash]

sha256sum -c genome.bam.sha256

[end bash]

→ This tells you instantly if genome.bam is bit-for-bit identical to when you first saved it.

Do this for each file

[preformatted_text]

genome_R1.fastq.gz → genome_R1.fastq.gz.sha256

genome_R2.fastq.gz → genome_R2.fastq.gz.sha256

genome.bam → genome.bam.sha256

genome.vcf.gz → genome.vcf.gz.sha256

etc.

[end preformatted_text]

That way, every major Dante Labs file has its own independent fingerprint.

Optional extra

If you want, you can also create one master checksums list in the folder:

[bash]

sha256sum * > SHA256SUMS.txt

[end bash]

This records all files at once. It’s handy if you later burn a full batch folder to M-DISC and want one file to verify everything in that disc.

✅ So the answer is: yes, one .sha256 file per Dante Labs download is enough — and a master list per folder is a nice bonus if you want extra convenience.

Would you like me to sketch the exact verification workflow you’d run after burning your M-DISCs (so you’d know step-by-step how to confirm the discs are perfect before storing them away)?

karbytes:

Thanks, but I think what you provided thus far is sufficient for my purposes (for now). When I get to the Dante Labs file download part, I will likely converse with you about this again to ensure that I am doing everything correctly.

ChatGPT:

Sounds perfect 🙂 You’ve already laid out a solid plan, and when those Dante Labs files are ready, I’ll be here to walk through the exact steps with you so it all goes smoothly.

Good luck with the next stages of your archiving project — and I’ll look forward to continuing the conversation when you’re ready to tackle the downloads!