Tag: linked-read

If you have single-sample data, you might be interested in a genome assembly. Unlike metagenome assemblies, a classic genome assembly assumes there is exactly one genome present in your sequences and will try to assemble the most contiguous sequences for this one individual.

Running is optional. In the alignment workflows ( ), Harpy already uses a distance-based approach to deconvolve barcodes and assign MI tags (Molecular Identifier). This workflow uses a reference-free method, QuickDeconvolution, which uses k-mers to look at "read clouds" (all reads with the same linked-read barcode) and decide which ones likely originate from different molecules. Regardless of whether you run this workflow or not, will still perform its own deconvolution.

Sequences that come off of a sequencer are typically not ready for analytical use. If the data hasn't already been separated out by sample, you need to do that first. If you're using linked-read data, you then need to identify the linked-read barcodes and pull them out of the sequence. Below describes the workflows for the original haplotagging protocol published by Meier et al. (2021), along with the one we're developed at Cornell University (Iqbal et al., in prep).

After variants have been called, you may want to impute missing genotypes to get the most from your data. Harpy uses STITCH to impute genotypes, a haplotype-based method that is linked-read aware. Imputing genotypes requires a variant call file containing SNPs, such as that produced by and preferably filtered in some capacity. You can impute genotypes with Harpy using the module:

If you have mixed-sample data, you might be interested in a metagenome assembly, also known as a metassembly. Unlike a single-sample assembly, a metassembly assumes there are multiple genomes present in your sequences and will try to assemble the most contiguous sequences for multi-sample (or multi-species) data.

Harpy does a lot of stuff with a lot of software and each of these programs expect the incoming data to follow particular formats (plural, unfortunately). These formatting opinions/specifics are at the mercy of the original developers and while there are times when Harpy can (and does) modify input/output files for format compatability, it's not always feasible or practical to handle all possible cases. So, our solution is perform what we lovingly call "pre-flight checks" to assess if your input FASTQ or BAM files are formatted correctly for the pipeline. There are separate and submodules and the result of each is a report detailing file format quality checks.

Raw sequences are not suitable for downstream analyses. They have sequencing adapters, index sequences, regions of poor quality, etc. The first step of any genetic sequence analyses is to remove these adapters and trim poor quality data. You can remove adapters, remove duplicates, and quality trim sequences using the module:

After reads have been aligned, e.g., with , you can use those alignment files (.bam) to call variants in your data. Harpy can call SNPs and small indels using bcftools mpileup or with freebayes. You can call SNPs with the module: