Crate paraseq 

paraseq

A high-performance Rust library for parallel processing of FASTA/FASTQ (FASTX) sequence files, optimized for modern hardware and large datasets.

paraseq provides a simplified interface for parallel processing of FASTX files that can be invariant to the file format or paired status.

Summary

paraseq is built specifically for processing paired-end FASTA/FASTQ sequences in a parallel manner. It uses RecordSets as the primary unit of buffering, with each set directly reading a fixed number of records from the input stream. To handle incomplete records it uses the shared Reader as an overflow buffer. It adaptively manages buffer sizes based on observed record sizes, estimating the required space for the number of records in the set and minimizing the number of copies between buffers. paraseq is most efficient in cases where the variance in record sizes is low as it can accurately minimize the number of copies between buffers.

It matches performance of non-record-set parsers (like seq_io and needletail), but can get higher-throughput in record-set contexts (i.e. multi-threaded contexts) by skipping a full copy of the input.

See benchmarking repo for benchmarking implementation.

If you’re interested in reading more about it, I wrote a small blog post discussing its design and motivation.

Features

• High performance parsing with minimal-copy of input data.
• Multi-line parsing support for FASTA
• Parallel processing of single-end, paired-end, interleaved, and multi-FASTX files with a consistent API.
• Simple construction of readers from file paths and handles with optional transparent decompression support with niffler.
• Generalized Map-Reduce pattern for processing sequencing data (single-end, paired-end, and interleaved)

Optional Features (Feature Flags)

• Supports parallel processing of SAM/BAM/CRAM files using rust_htslib (with htslib feature flag)
• Supports URLs as input for FASTX files over HTTP and HTTPS (with url feature flag)
• Supports SSH paths as inputs respecting system configuration (with ssh feature flag)
• Supports Google Cloud Storage (GCS) URIs (with gcs feature flag). requires gcloud to be installed and authenticated

Usage

The benefit of using paraseq is that it makes it easy to distribute paired-end records to per-record and per-batch processing functions.

Code Examples

Check out the examples directory for code examples or the API documentation for more information.

Feel free to also explore seqpls a parallel paired-end FASTA/FASTQ sequence grepper to see how paraseq can be used in a non-toy example.

Basic Usage

This is a simple example using paraseq to iterate records in a single-threaded context.

It is not recommended to use paraseq in this way - it will be more performant to use the parallel processing interface.

use std::fs::File;
use paraseq::{fastq, Record};

fn main() -> Result<(), paraseq::Error> {
    let path = "./data/sample.fastq";
    let mut reader = fastq::Reader::from_path(path)?;
    let mut record_set = reader.new_record_set();

    while record_set.fill(&mut reader)? {
        for record in record_set.iter() {
            let record = record?;
            // Process record...
            println!("ID: {}", record.id_str());
        }
    }
    Ok(())

}

Parallel Processing

To distribute processing across multiple threads you can implement the ParallelProcessor trait on your arbitrary struct.

For an example of a single-end parallel processor see the parallel example.

use std::fs::File;
use paraseq::{fastx, ProcessError};
use paraseq::prelude::*;

#[derive(Clone, Default)]
struct MyProcessor {
// Your processing state here
}

impl ParallelProcessor for MyProcessor {
    fn process_record<R: Record>(&mut self, record: R) -> Result<(), ProcessError> {
        // Process record in parallel
        Ok(())
    }
}

fn main() -> Result<(), ProcessError> {
    let path = "./data/sample.fastq";
    let reader = fastx::Reader::from_path(path)?;
    let processor = MyProcessor::default();
    let num_threads = 8;

    reader.process_parallel(processor, num_threads)?;
    Ok(())

}

Paired-End Processing

Paired end processing is as simple as single-end processing, but involves implementing the PairedParallelProcessor trait on your struct.

For an example of paired parallel processing see the paired example.

use std::fs::File;
use paraseq::{
    fastx,
    ProcessError,
    prelude::*,
};

#[derive(Clone, Default)]
struct MyPairedProcessor {
    // Your processing state here
}

impl PairedParallelProcessor for MyPairedProcessor {
    fn process_record_pair<R: Record>(&mut self, r1: R, r2: R) -> Result<(), ProcessError> {
        // Process paired records in parallel
        Ok(())
    }
}

fn main() -> Result<(), ProcessError> {
    let path1 = "./data/r1.fastq";
    let path2 = "./data/r2.fastq";

    let reader1 = fastx::Reader::from_path(path1)?;
    let reader2 = fastx::Reader::from_path(path2)?;
    let processor = MyPairedProcessor::default();
    let num_threads = 8;

    reader1.process_parallel_paired(reader2, processor, num_threads)?;
    Ok(())
}

Interleaved Processing

Interleaved processing is also supported by implementing the InterleavedParallelProcessor trait on your struct.

For an example of interleaved parallel processing see the interleaved example.

use std::fs::File;
use paraseq::{
    fastx,
    ProcessError,
    prelude::*,
};

#[derive(Clone, Default)]
struct MyInterleavedProcessor {
    // Your processing state here
}

impl InterleavedParallelProcessor for MyInterleavedProcessor {
    fn process_interleaved_pair<R: Record>(&mut self, r1: R, r2: R) -> Result<(), ProcessError> {
        // Process interleaved paired records in parallel
        Ok(())
    }
}

fn main() -> Result<(), ProcessError> {
    let path = "./data/interleaved.fastq";
    let reader = fastx::Reader::from_path(path)?;
    let processor = MyInterleavedProcessor::default();
    let num_threads = 8;

    reader.process_parallel_interleaved(processor, num_threads)?;
    Ok(())
}

Limitations

1. In cases where records have high variance in size, the buffer size predictions will become inaccurate and the shared Reader overflow buffer will incur more copy operations. This may lead to suboptimal performance.

2. Multiline FASTA is now supported! However, you will incur additional memory usage when calling the Record::seq() method with multiline FASTA - as it incurs an allocation to remove newlines from the sequence. You can choose to avoid this by using the newly introduced Record::seq_raw() method which will return the sequence buffer without any modifications (though it may have newlines). Here there be dragons!

3. Each RecordSet maintains its own buffer, so memory usage practically scales with the number of threads and record capacity. This shouldn’t be a concern unless you’re running this on a system with very limited memory.

License

MIT

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Similar Projects

This work is inspired by the following projects:

• seq_io
• fastq

This project aims to be directed more specifically at ergonomically processing of paired records in parallel and is optimized mainly for FASTQ files. It can be faster than seq_io for some use cases, but it is not currently as rigorously tested. However, it now supports both single-line and multi-line FASTA files with optimized performance.

If the libraries assumptions do not fit your use case, you may want to consider using seq_io or fastq instead.

Re-exports

pub use parallel::ProcessError;

Modules

fasta

fastq

fastx

parallel

prelude

Enums

Error

Constants

DEFAULT_MAX_RECORDS

Default maximum number of records in a record set.

MAX_ARITY

The maximum number of record segments supported in a single synchronized record group. For example, single-end reads have arity 1, paired-end reads have arity 2, etc.

Traits

Record