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Highlighting some releases in July 2023

· 5 min read
Trent Hauck
Trent Hauck
Developer

We wanted to highlight three updates that we made in July 2023.

First, Exon now will rewrite queries to scan multiple files in parallel. This enables considerable speedups for queries over large datasets -- both for local files and object stores (e.g. S3, GCS, etc.).

Second, we're releasing an initial version of an MzML reader in our suite of tools. This is our first step towards supporting proteomics and metabolomics data in exon. This format is subject to change as we get feedback from users, so please let us know what you think!

Third, we're adding support for pandas dataframes in BioBear. This is a common request, so we're excited to be able to support it.

Parallel Scan​

Not only is going fast fun, but it's also important for enabling the fast iteration cycles necessary for innovation. To that end, Exon and related tools will now automatically parallelize scans over multiple files.

For example, if you have a directory with FASTA files, you can now do the following:

import biobear as bb
from pathlib import Path

data = Path("./data/") # folder with fasta files
# note: FastaReader is now deprecated, use session.read_fasta_file(data)
df = FastaReader(data).to_polars()  # need to install polars

And Exon will distribute reading the files across available cores. This is a huge win for data lakes, where you can now read multiple files in parallel without having to manually shard your data. E.g. here we see a 4x speed-up when reading 8 files.

drawing

MzML Reader​

MzML is a very common format in metabolomics and proteomics. At its core are measurements of the spectrum from the mass spec, but there's also a Controlled Vocabulary that describes various metadata about the experiment.

Similar to other readers, you instantiate a reader and then depending on what you want to do with it, you can convert it to arrow or polars with BioBear, or a data.frame with R.

import biobear as bb

# object stores and local files are supported
df = bb.MzMLReader("mzml-data/GNPS00002_A3_p.mzML").to_polars()

df.head()
# shape: (5, 6)
# ┌───────────────────────────────────┬───────────────────────────────────┬───────────────────────────────────┬────────────┬───────────────────────────────────┬────────────────┐
# │ id                                ┆ mz                                ┆ intensity                         ┆ wavelength ┆ cv_params                         ┆ precursor_list │
# │ ---                               ┆ ---                               ┆ ---                               ┆ ---        ┆ ---                               ┆ ---            │
# │ str                               ┆ struct[1]                         ┆ struct[1]                         ┆ f32        ┆ object                            ┆ object         │
# ╞═══════════════════════════════════╪═══════════════════════════════════╪═══════════════════════════════════╪════════════╪═══════════════════════════════════╪════════════════╡
# │ controllerType=0 controllerNumbe… ┆ {[80.948143, 80.993134, … 957.16… ┆ {[5416.88916, 7712.663574, … 480… ┆ null       ┆ [('MS:1000579', {'accession': 'M… ┆ None           │
# │ controllerType=0 controllerNumbe… ┆ {[80.284782, 81.017242, … 1176.3… ┆ {[4701.711426, 5527.709961, … 95… ┆ null       ┆ [('MS:1000579', {'accession': 'M… ┆ None           │
# │ controllerType=0 controllerNumbe… ┆ {[80.948288, 81.017365, … 1193.9… ┆ {[11152.501953, 9900.050781, … 8… ┆ null       ┆ [('MS:1000579', {'accession': 'M… ┆ None           │
# │ controllerType=0 controllerNumbe… ┆ {[80.948143, 81.017372, … 1074.1… ┆ {[6677.85791, 11401.181641, … 12… ┆ null       ┆ [('MS:1000579', {'accession': 'M… ┆ None           │
# │ controllerType=0 controllerNumbe… ┆ {[80.948174, 81.017387, … 968.84… ┆ {[6586.833984, 13542.833008, … 6… ┆ null       ┆ [('MS:1000579', {'accession': 'M… ┆ None           │
# └───────────────────────────────────┴───────────────────────────────────┴───────────────────────────────────┴────────────┴───────────────────────────────────┴────────────────┘

For R users, see the read_mzml_file function. And for exon-duckdb users see

Please see the docs for more information about MzML files, and again, please let us know if you have any feedback!

A Sneak Peak for Querying​

While it's not quite ready yet for BioBear and ExonR, in Exon, we've added a set of MzML specific functions for querying. For example, you can filter spectra based on the presence of a specific peaks.

For example, this will filter a set of spectra to only those that have a peak at 100.0 m/z that's within 0.1 m/z of the peak.

SELECT id
FROM mzml -- same table as above
WHERE contains_peak(mz.mz, 100.0, 0.1) = true

This sort of declarative informatics is part of the Exon vision, which we'll be talking more about in the future.

A Note on Performance​

Similar to BioBear comparing favorably to other scientific data libraries, we see modest out-performance for MzML file reading.

The following compares reading using a few difference libraries in Python on an MzML file with about 8k spectra.

drawing

Pandas Support in BioBear​

There's not much to say besides that places where you could use to_polars you can now also use to_pandas. E.g.

import biobear as bb
from pathlib import Path

data = Path("./data/uniref50.fasta")
# note: FastaReader is now deprecated, use session.read_fasta_file(data)
df = FastaReader(data).to_pandas()  # need to install pandas

df.head()
#                     id                                        description                                           sequence
# 0  UniRef50_A0A5A9P0L4  peptidylprolyl isomerase n=1 Tax=Triplophysa t...  MEEITQIKKRLSQTVRLEGKEDLLSKKDSITNLKTEEHVSVKKMVI...
# 1  UniRef50_A0A410P257  Glycogen synthase n=2 Tax=Candidatus Velamenic...  MKAIAWLIVLTFLPEQVAWAVDYNLRGALHGAVAPLVSAATVATDG...
# 2  UniRef50_A0A8J3NBY6  Gln_amidase domain-containing protein n=2 Tax=...  MEILGRNLPRILGNLVKTIKTAPVRVVARRGARTLTQKEFGKYLGS...
# 3      UniRef50_Q8WZ42  Titin n=3053 Tax=cellular organisms TaxID=1315...  MTTQAPTFTQPLQSVVVLEGSTATFEAHISGFPVPEVSWFRDGQVI...
# 4  UniRef50_A0A401TRQ8  Ig-like domain-containing protein (Fragment) n...  PPSFIHKPDPQEVLPGSNVKFTSVVTGTAPLKVSWFKGTTELVAGR...

Conclusion​

We hope you enjoy the new features and please let us know if you have any feedback! We'll be back next month with more updates.

ExonR

· One min read
Trent Hauck
Trent Hauck
Developer

We're excited release ExonR. This is an R package that allows for interacting with Exon through an ergonomic R interface.

Usage​

As a quick example, we can read in a GFF file from S3 and convert it to a GRanges object.

library(exonr)
library(GenomicRanges)

rdr <- read_gff_file(
  "s3://bucket/path/to/file.gff",
)

df <- as.data.frame(rdr)

gr <- GRanges(
  seqnames = df$seqname,
  ranges = IRanges(
    start = df$start,
    end = df$end,
  ),
)

Installation​

If you're on an x64 machine, you should be able to install a pre-built binary from our R-universe.

install.packages('exonr', repos = c('https://wheretrue.r-universe.dev', 'https://cloud.r-project.org'))

This should also build on other platforms, but you'll need to have Rust installed. The main Rust website has a guide for installing rust. Once you have rust installed, you can install ExonR from source with the command above.