A machine-readable specification for genomics assays

A machine-readable specification for

genomics assays

A. Sina Booeshaghi

, Xi Chen

, and Lior Pachter

1,3*

1. Division of Biology and Biological Engineering, California Institute of Technology,

Pasadena, California

2. School of Life Sciences, Southern University of Science and Technology, Shenzhen,

China

3. Department of Computing and Mathematical Sciences, California Institute of Technology,

Pasadena, California

*Address correspondence to

abooesha@caltech.edu

& lpachter@caltech.edu

Abstract

Understanding the structure of sequenced fragments from genomics libraries is essential for

accurate read preprocessing. Currently, different assays and sequencing technologies require

custom scripts and programs that do not leverage the common structure of sequence elements

present in genomics libraries. We present

seqspec,

a machine-readable specification for

libraries produced by genomics assays that facilitates standardization of preprocessing and

enables tracking and comparison of genomics assays. The specification and associated

seqspec

command line tool is available at

https://github.com/IGVF/seqspec.

Introduction

The proliferation of genomics assays (Ogbeide et al. 2022) has resulted in a corresponding

increase in software for processing the data (Zappia, Phipson, and Oshlack 2018). Frequently,

custom scripts must be created and tailored to the specifics of assays, where developers

reimplement solutions for common preprocessing tasks such as adapter trimming, barcode

identification, error correction, and read alignment (Wu et al. 2022; Ma et al. 2020; Cheow et al.

2016; Healey, Bassham, and Cresko 2022). When software tools are assay specific, parameter

choices in these methods can diverge, making it difficult to perform apples-to-apples

comparisons of data produced by different assays. Furthermore, the lack of preprocessing

standardization makes reanalysis of published data in the context of new data challenging.

While genomics protocols can vary greatly from each other, the libraries they generate share

many common elements. Typically, sequenced fragments will contain one or several “technical

sequences” such as barcodes and unique molecular identifiers (UMIs), as well as biological

sequences that may be aligned to a genome or transcriptome. Standard library preparation kits

generally require that DNA from the libraries is cut, repaired, and ligated to sequencing adapters

(Figure 1). Primers bind to the sequencing adapters, and initiate DNA sequencing whereby

reads are subsequently generated. Illumina sequencing employs a sequencing by synthesis

approach where fluorescently labeled nucleotides are incorporated into single-stranded DNA,

and imaged, while PacBio uses zero-mode waveguides for single-molecule detection of dNTP

incorporation. Oxford Nanopore on the other hand binds sequencing adapters to pores in a flow

cell and DNA is sequenced by changes in electrical resistance across the pore (Iizuka,

Yamazaki, and Uemura 2022).

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Figure 1: The structure of reads sequenced from genomics libraries.

Sequencing libraries

are constructed by combining Atomic

Regions

to form an adapter-insert-adapter construct. The

seqspec

for the assay annotates the construct with

Regions

and

Meta Regions.

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Many single-cell genomics assays introduce additional library complexity further complicating

preprocessing. For example, the inDropsv3 (Klein et al. 2015) assay produces variable length

barcodes while the 10x Genomics scRNA-seq assay (Zheng et al. 2017) produces fixed-length

barcodes that are derived from a known list of possibilities.

Current file formats such as FASTQ, Genbank, FASTA, and workflow-specific files (Parekh et al.

2018) lack the flexibility to annotate sequenced reads that contain these complex features. In

the absence of sequence annotations, processing can be challenging, limiting the reuse of data

that is stored in publicly accessible databases such as the Sequence Read Archive (Katz et al.

2022). To facilitate utilization of genomics data, a database of assays along with a description of

their associated library structures was assembled in (Chen 2020). While this database has

proved to be very useful, the HTML descriptors are not machine readable. Moreover, the lack of

a formal specification limits the utility and expandability of the database.

Results

The

seqspec

specification defines a machine-readable file format, based on YAML, that enables

sequence read annotation. Reads are annotated by

Regions

which can be nested and

appended to create a

seqspec

Regions

are annotated with a variety of properties that simplify

the downstream identification of sequenced elements. The following are a list of properties that

can be associated with a

Region

● Region ID: unique identifier for the

Region

in the

seqspec

● Region type: the type of region

● Name: A descriptive name for the

Region

● Sequence: The specific nucleotide sequence for the

Region

● Sequence type: The type of sequence (fixed, onlist, random, joined)

● Minimum length: The minimum length of the sequence for the

Region

● Maximum length: The maximum length of the sequence for the

Region

● Onlist: The list of permissible sequences from which the Sequence is derived

Importantly,

Regions

, known as meta

Regions

, can contain

Regions

; a property that is useful for

grouping and identifying sequence types that are contained in reads. The YAML format is a

natural language to represent nested meta-

Regions

in a human-readable fashion. Python-style

indentation and syntax can be used to create a human-readable file format without the

excessive grouping delimiters of alternative languages such as JSON. Additionally, nested

Regions

allow Assays to be represented as an Ordered Tree where the ordering of subtrees is

significant: atomic

Regions

are “glued” together in an order that is concordant with the design of

the sequencing library.

Importantly,

seqspec

files are machine-readable, and

Region

data can be parsed, processed,

and extracted with the

seqspec

command-line tool. The tool contains six subcommands that

enable various tasks such as specification checking, finding, formatting, and indexing,

1. seqspec check

: check the correctness of attributes against the

seqspec

shema

2. seqspec find

: print

Region

metadata

3. seqspec format

: auto populate

Region

metadata for meta

Regions

4. seqspec index

: extract the 0-indexed position of

Regions

5. seqspec print

: print an html or markdown file that visualizes the

seqspec

6. seqspec split

: split a FASTQ file by

Regions

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Figure 2: Uniform processing enabled with

seqspec

The

seqspec index

command produces

a technology string that identifies appropriate sequence elements and can be passed into

processing tools.

To illustrate how

seqspec

can be used to facilitate processing and analysis of single-cell

RNA-seq reads, we implemented in the

seqspec index

command the facility to produce the

relevant technology string for three single-cell RNA-seq preprocessing tools: kallisto bustools

(Melsted et al. 2021), simpleaf/alevin-fry (He et al. 2022), and STARsolo (Kaminow, Yunusov,

and Dobin 2021) (Figure 2).

Regions

associated with barcodes, UMIs, and cDNA are extracted,

positionally indexed and formatted on a per-tool basis. The modularity of

seqspec

makes it

simple to produce tool-compatible technology strings for other assay types.

Discussion

Standardized annotation of sequencing reads in a human- and machine-readable format serves

several purposes including the enablement of uniform processing, organization of sequencing

assays by constitutive components, and transparency for users. The flexibility of

seqspec

should allow it to be used for all current sequence census assays (Wold and Myers 2008), and

specifications should be readily adaptable to different sequencing platforms; our initial release of

seqspec

contains specifications for 38 assays (see

https://igvf.github.io/seqspec/).

Comparison of

seqspec

s for different assays, immediately reveals shared similarities and

differences. For example, the SPLiT-seq single-cell RNA and the multimodal SHARE-seq

single-cell assays are aimed at different modalities and utilize different protocols to produce

libraries, but the resultant structures are very similar (Figure 1) since they both rely on split-pool

barcoding (Rosenberg et al. 2018). The

seqspec

for the sci-CAR-seq assay (Cao et al. 2018),

from which split-pool assays such as SHARE-seq are derived, shows that the cell barcoding is

encoded in the Illumina indices. It should be possible to develop an ontology of assays by

comparing the

seqspec

specifications of assays and quantifying their similarities and

differences.

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In demonstrating that

seqspec

can be used to define options for preprocessing tools, we have

shown that

seqspec

is immediately useful for uniform processing of genomics data. The

preprocessing applications will hopefully incentivize data generators to define and deposit

seqspec

files alongside sequencing reads in public archives such as the Sequence Read

Archive. While

seqspec

is not a suitable format for general metadata storage, the precise

specification of sequence elements present in reads, including sequencer-specific constructs,

should be helpful in identifying batch effects even when metadata is missing or inaccurate.

Acknowledgements

We thank Delaney Sullivan for helpful discussions and Rahma Elsiesy for helpful feedback on

Figure 1. Discussions with the Impact of Genomics Variation on Function (IGVF) Single-Cell

Focus Group helped to shape some features of

seqspec

. Thanks to Idan Gabdank for useful

feedback on

seqspec

and for suggesting the md5 checksum

Meichen Fang contributed the

sci-RNA-seq3

seqspec.

A.S.B. and L.P. were supported in part by NIH

5UM1HG012077-02.

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