In recent years we have witnessed a growth in sequencing yield, the number of samples sequenced, and as a result–the growth of publicly maintained sequence databases. The increase of data present all around has put high requirements on protein similarity search algorithms with two ever-opposite goals: how to keep the running times acceptable while maintaining a high-enough level of sensitivity. The most time consuming step of similarity search are the local alignments between query and database sequences. This step is usually performed using exact local alignment algorithms such as Smith-Waterman. Due to its quadratic time complexity, alignments of a query to the whole database are usually too slow. Therefore, the majority of the protein similarity search methods prior to doing the exact local alignment apply heuristics to reduce the number of possible candidate sequences in the database. However, there is still a need for the alignment of a query sequence to a reduced database. In this paper we present the SW#db tool and a library for fast exact similarity search. Although its running times, as a standalone tool, are comparable to the running times of BLAST, it is primarily intended to be used for exact local alignment phase in which the database of sequences has already been reduced. It uses both GPU and CPU parallelization and was 4–5 times faster than SSEARCH, 6–25 times faster than CUDASW++ and more than 20 times faster than SSW at the time of writing, using multiple queries on Swiss-prot and Uniref90 databases

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This repository contains the input databases and summarized results for evaluating our preprint:

raxtax: A k-mer-based non-Bayesian Taxonomic Classifier (BioRxiv)

Abstract

Motivation: Taxonomic classification in biodiversity studies is the process of assigning the anonymous sequences of
a marker gene (barcode) to a specific lineage using a reference database that contains named sequences in a known
taxonomy. This classification is important for assessing the complexity of biological systems. Taxonomic classification
faces two inherent challenges: first, accuracy is critical as errors can propagate to downstream analysis results; and
second, the classification time requirements can limit study size and study design, in particular when considering
the constantly growing reference databases. To address these two challenges, we introduce raxtax, an efficient, novel
taxonomic classification tool that uses common k-mers between all pairs of query and reference sequences. We also
introduce two novel uncertainty scores which take into account the fundamental biases of reference databases.
Results: We validate raxtax on three widely used empirical reference databases and show that it is 2.7-100 times faster
than competing state-of-the-art tools on the largest database while being equally accurate. In particular, raxtax exhibits
increasing speedups with growing query and reference sequence numbers compared to existing tools (for 100,000 and
1,000,000 query and reference sequences overall, it is 1.3 and 2.9 times faster, respectively), and therefore alleviates the
taxonomic classification scalability challenge.
Availability and Implementation: raxtax is available at https://github.com/noahares/raxtax under a CC-NC-
BY-SA license. The scripts and summary metrics used in our analyses are available at https://github.com/noahares/
raxtax_paper_scripts.

Original Data Sources

UNITE: https://doi.plutof.ut.ee/doi/10.15156/BIO/2959332

Greengenes: http://ftp.microbio.me/greengenes_release/gg_13_5/

BOLD: https://boldsystems.org/ (exact database version no longer available)

Fisher’s exact test results according to the presence and absence of hiccups and the route of dexamethasone administration.

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Replication package for the paper:

Ludovic Courtès, Timothy Sample, Simon Tournier, Stefano Zacchiroli.Source Code Archiving to the Rescue of Reproducible DeploymentACM REP'24, June 18-20, 2024, Rennes, Francehttps://doi.org/10.1145/3641525.3663622

Generating the paper

The paper can be generated using the following command:

guix time-machine -C channels.scm
-- shell -C -m manifest.scm
-- make

This uses GNU Guix to run make in the exact same computational environment used when preparing the paper. The computational environment is described by two files. The channels.scm file specifies the exact version of the Guix package collection to use. The manifest.scm file selects a subset of those packages to include in the environment.

It may be possible to generate the paper without Guix. To do so, you will need the following software (on top of a Unix-like environment):

GNU Make

SQLite 3

GNU AWK

Rubber

Graphviz

TeXLive

Structure

data/ contains the data examined in the paper

scripts/ contains dedicated code for the paper

logs/ contains logs generated during certain computations

Preservation of Guix

Some of the claims in the paper come from analyzing the Preservation of Guix (PoG) database as published on January 26, 2024. This database is the result of years of monitoring the extent to which the source code referenced by Guix packages is archived. This monitoring has been carried out by Timothy Sample who occasionally publishes reports on his personal website: https://ngyro.com/pog-reports/latest/. The database included in this package (data/pog.sql) was downloaded from https://ngyro.com/pog-reports/2024-01-26/pog.db and then exported to SQL format. In addition to the SQL file, the database schema is also included in this package as data/schema.sql.

The database itself is largely the result of scripts, but also of manual adjustments (where necessary or convenient). The scripts are available at https://git.ngyro.com/preservation-of-guix/, which is preserved in the Software Heritage archive as well: https://archive.softwareheritage.org/swh:1:snp:efba3456a4aff0bc25b271e128aa8340ae2bc816;origin=https://git.ngyro.com/preservation-of-guix. These scripts rely on the availability of source code in certain locations on the Internet, and therefore will not yield exactly the same result when run again.

Analysis

Here is an overview of how we use the PoG database in the paper. The exact way it is queried to produce graphs and tables for the paper is laid out in the Makefile.

The pog-types.sql query gives the counts of each source type (e.g. “git” or “tar-gz”) for each commit covered by the database.

The pog-status.sql query gives the archival status of the sources by commit. For each commit, it produces a count of how many sources are stored in the Software Heritage archive, missing from it, or unknown if stored or missing. The pog-status-total.sql query does the same thing but over all sources without sorting them into individual commits.

The disarchive-ratio.sql query estimates the success rate of Disarchive disassembly.

Finally, the swhid-ratio.sql query gives the proportion of sources for which the PoG database has an SWHID.

Estimating missing sources

The Preservation of Guix database only covers sources from a sample of commits to the Guix repository. This greatly simplifies the process of collecting the sources at the risk of missing a few. We estimate how many are missed by searching Guix’s Git history for Nix-style base-32 hashes. The result of this search is compared to the hashes in the PoG database.

A naïve search of Git history results in an over estimate due to Guix’s branch development model. We find hashes that were never exposed to users of ‘guix pull’. To work around this, we also approximate the history of commits available to ‘guix pull’. We do this by scraping push events from the guix-commits mailing list archives (data/guix-commits.mbox). Unfortunately, those archives are not quite complete. Missing history is reconstructed in the data/missing-links.txt file.

This estimate requires a copy of the Guix Git repository (not included in this package). The repository can be obtained from GNU at https://git.savannah.gnu.org/git/guix.git or from the Software Heritage archive: https://archive.softwareheritage.org/swh:1:snp:9d7b8dcf5625c17e42d51357848baa226b70e4bb;origin=https://git.savannah.gnu.org/git/guix.git. Once obtained, its location must be specified in the Makefile.

To generate the estimate, use:

guix time-machine -C channels.scm
-- shell -C -m manifest.scm
-- make data/missing-sources.txt

If not using Guix, you will need additional software beyond what is used to generate the paper:

GNU Guile

GNU Bash

GNU Mailutils

GNU Parallel

Measuring link rot

In order to measure link rot, we ran Guix Scheme scripts, i.e., scripts that exploit Guix as a Scheme library. The scripts depend on the state of world at the very specific moment when they ran. Hence, it is not possible to reproduce the exact same outputs. However, their tendency over the passing of time should be very similar. For running them, you need an installation of Guix. For instance,

guix repl -q scripts/table-per-origin.scm

When running these scripts for the paper, we tracked their output and saved it inside the logs directory.

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To achieve accurate assignment of peptide sequences to observed fragmentation spectra, a shotgun proteomics database search tool must make good use of the very high-resolution information produced by state-of-the-art mass spectrometers. However, making use of this information while also ensuring that the search engine’s scores are well calibrated, that is, that the score assigned to one spectrum can be meaningfully compared to the score assigned to a different spectrum, has proven to be challenging. Here we describe a database search score function, the “residue evidence” (res-ev) score, that achieves both of these goals simultaneously. We also demonstrate how to combine calibrated res-ev scores with calibrated XCorr scores to produce a “combined p value” score function. We provide a benchmark consisting of four mass spectrometry data sets, which we use to compare the combined p value to the score functions used by several existing search engines. Our results suggest that the combined p value achieves state-of-the-art performance, generally outperforming MS Amanda and Morpheus and performing comparably to MS-GF+. The res-ev and combined p-value score functions are freely available as part of the Tide search engine in the Crux mass spectrometry toolkit (http://crux.ms).

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