# An introduction to linked-read data

Harpy is a software suite tailor-made for haplotagging linked read data. BRL/LRTK/LongRanger are similar pieces of software for Tellseq, stFLR, and 10X linked-read data. But, what if you don't use linked reads (yet) and want to understand what it actually is? This post walks you through what linked-read data is and some of the concepts that are unique to it that makes it different than your typical short-read data. Although Harpy specializes in Haplotagging linked-read data, what we're discussing here is linked reads as a whole.

• a cup of tea, coffee, or water
• a can-do attitude

# Linked reads

Let's start with the most obvious, what are linked reads, which are sometimes called "synthetic long reads"? They are short read (e.g. Illumina) data. What makes them different is that they contain an added DNA segment ("barcode") that lets us associate sequences as having originated from a single DNA molecule. That means if we have 4 sequences that all contain the same added barcode, then we infer that they must have originated from the same original DNA molecule. Different barcode = different molecule of origin. If the sequences would map to the same genomic region during sequence alignment, we would know that those sequences with the same barcode originated from a single DNA fragment from a single homologous chromosome from a single cell. That's right, built-in phase information.

# What do they look like?

Linked-read data is sequence data as you would expect it, encoded in a FASTQ file. The first processing step of linked-read data is demultiplexing to split the raw Illumina-generated batch FASTQ file into samples (if multisample) and identify/validate the linked-read barcode on every sequence. For 10X data, the barcode would stay inline with the sequence (to make it LongRanger compatible), but for other varieties (haplotagging, stFLR, etc.) you would also remove the barcode from the sequence and preserve it using the BX:Z tag in the sequence headers. The demultiplexing process is generally similar between non-10X linked-read technologies: a nucleotide barcode gets identified and moved from the sequence to the read header under the BX:Z tag. The diagram below preserves the nucleotide barcode under the OX:Z tag and recodes it under BX:Z using the haplotagging "ACBD" segment-aware format, however it would also be valid to just keep the nucleotide barcode under BX:Z. Linked-read software is variable in its flexibility towards barcode formatting.

# Deconvolution

There are approaches to deconvolve linked read data (or "deconvolute", if you're indifferent to the burden of an extra syllable). In this context, deconvolution is finding out which sequences are sharing barcodes by chance rather than because they originated from the same DNA molecule. The likelihood of it happening is usually low, but it's not impossible. When the algorithm determines that barcodes are being shared by unrelated molecules, the barcode typically gets suffixed with a hyphenated number (e.g. BX:Z:ATACG becomes BX:Z:ATACG-1). As of this writing, there are only a few pieces of software that can deconvolve linked-read data and do so with varying degrees of success and computational resource requirements. Similarly, linked-read software is variable in its flexibility towards accepting the deconvolved-barcode format.

# Linked-read varieties

There are a handful of linked-read sample preparation methods (haplotagging, stFLR, etc.), but that's largely an implementation detail. All of those methods are laboratory procedures to take genomic DNA and do the necessary modifications to fragment long DNA molecules, tag the resulting fragments with the same DNA barcode, then add the necessary Illumina adapters. It's not unlike the different RAD flavors (e.g. EZrad, ddRAD, 2B-rad)-- they all give you RAD data in the end, but vary in how you get there in terms of cost and bench time. We obviously subscribe to haplotagging 😁.

# Failsafe

Unlike some other fancy well-touted sample preparation methods (cough cough mate-pair), linked-read data is whole genome sequencing (WGS). What that means is that the data, whether you use the linked-read information or not, will always be standard and viable WGS compatible with whatever you would use WGS for. It's WGS, but with a little extra info that goes a long way.

# Linked-read library performance

Because linked-read data has an extra dimension, there are some additional metrics that are useful to look at to evaluate how "good" the linked-read library turned out. As a baseline for comparison, think of RAD or WGS data and the kinds of metrics you might look at for library performance:

• coverage depth
• coverage breadth
• PCR/optical duplicates
• coverage depth per sample

Linked-read library performance also looks at that, but there's a few extra parameters that help us assess performance:

# Molecule Coverage

Since linked reads are tagger per DNA molecule, we are interested in understanding coverage on a per-molecule basis. By "molecule", we are referring to the original DNA molecule from which barcoded sequences originated from.

# reads per molecule

It's quite rare that all fragments of a single DNA molecule will get sequenced, so we are interested in getting an average number of reads (sequences) per original DNA molecule. This is done by getting counts of all the reads with the same barcode, as unique barcodes correspond with unique DNA molecules. It's difficult to say exactly what a good number of reads per molecule would be, but >2 would be a good starting point (1 would be a singleton, see below). Having 3-6 would be decent, depending on your project goals.

# percent molecule coverage

Because only a few fragments from a DNA molecule end up getting sequenced, it would be good to understand how much of a molecule is represented in seqences (breadth). It's impossible to get an accurate calculation for this metric because we have no way of knowing the size of the original DNA molecule, but what we can do is, after alignment, find the distance between the two furthest reads sharing a barcode and calculate how many bases between them have been sequenced. It's not perfect, but it's something. A low number would indicate large gaps between linked sequences (e.g. few sequences and/or a very large molecule), whereas a high number would indicate small gaps between linked sequences (e.g. many sequences and/or a very small molecule).

# Singletons

Because linked reads need to be, well, linked, we need to know exactly how many of the sequences actually share barcodes. A barcode that only appears in one paired or unpaired read is a singleton, meaning it isn't actually linked to any other sequence. Despite having a linked-read barcode, the absence of other reads with the same barcode means the barcode information for that read (or read pair) is mostly useless, aka it's just a plain-regular short read and can be used as such. Having <60% would be considered decent, but you would want as few singletons as possible in a linked-read dataset. For perspective, if 90% of your reads were singletons, you can think of that as "90% of your data aren't linked reads". There isn't yet a consensus for what to name the opposite, which is when a barcode is shared by more than one paired or unpaired read (a proper linked read), but our team calls them nonsingletons. It's admittedly clunky and we are open to suggestions.

# Linked Coverage

Because of the "linked" component of linked-read data, we have an additional kind of sequence coverage to consider, which is linked coverage. Since linked-read barcodes preserve phase information, we can do an alternative kind of coverage calculation where you pretend that all the sequences with a shared barcode are one big gapless sequence. If you use your imagination that way, the name "synthetic long reads" begins to make more sense. Mathematically, the linked coverage (breadth and depth) should always be higher than the coverage from just the alignments themselves, as gaps between linked reads are counted as well.

In real data, this actually looks very cool. Below is a graph from the per-sample alignment report Harpy generates. It's a circos plot, which is a circular representation of round charts, and each wedge is a chromosome (start to finish), labelled by the "2R", "2L", etc. around the perimeter. The inner circle (grey histogram) is the sequence alignment depth (i.e. the standard depth calculation) and the outer circle (magenta histogram) is the linked depth of those same data in those same 50kbp intervals. It has the most fascinating flower petal pattern, which is due to the lower likelihood of reads linking at the edges of chromosomes.