Correct and Reproducible Results

It is our goal to make the SNP Pipeline results both fully reproducible and as correct as current scientific understanding allows. As part of this effort, we document here problems we have found that have affected correctness. We also detail how we have built this software so the results are as reproducible as possible. In addition, we are building, collecting, and collating data sets that we use to assess both correctness and reproducibility of our software. As a project that is made possible only by very recent developments in science and technology, our efforts to ensure correctness and reproducibility are an ongoing effort. The publications we have produced in an effort to ensure scientific correctness are listed as references at the bottom of this document. This document will continue to evolve as we improve our process and as scientific advances occur.

Reproducible Results

We have made the SNP Pipeline results fully reproducible – not just the final SNP matrix, but each intermediate file as well. Reproducible results help us test and debug the pipeline and also facilitate collaborative efforts between researchers.

Public Availability

The SNP Pipeline source code is available on GitHub so anyone can download our source code. The GitHub repository contains some data sets that can be used to reproduce selected results. We also provide information on how to obtain and verify other data sets that we have used. (These data sets are large, so we do not provide them directly.)

Version Control

We use git internally for code development to ensure that we have control over our source code and can identify which version of code was used to produce any particular result. We tag/version commits of the code that we consider production releases and use them for the majority of our internal analyses. We also release each of these tagged versions to GitHub and to the Python Package Index for easy installation.


The SNP pipeline behavior depends on the setting of a number of parameters that determine the behavior of various software packages that the pipeline uses. These parameters affect both the correctness and reproducibility of results. We have set all the parameters so that the results are reproducible. This entails setting seeds for all random number dependent processes, as well as specific choices for other parameters that can affect such behavior as the order of the results. We discuss these aspects of ensuring reproducibility in more detail in other portions of this document.

The pipeline depends on some fairly complex software packages, and these packages have large numbers of parameters. As released, the pipeline does not specify values for every possible parameter, but only those we have found it useful to modify in our work. A configuration file used by the pipeline provides a record of the parameters used and also makes it possible to customize the behavior of the pipeline by adding or modifying parameters as needed. Those wishing to further modify the software behavior will have to adjust the code to meet their needs.

We recommend retaining the parameter values used for any important results, ideally in a script or configuration file that is under version control. The pipeline generates log files documenting each run by capturing software versions and parameters used for each run.


The SNP Pipeline takes advantage of multiple CPU cores to run portions of the processing in parallel. However, concurrency can lead to non-deterministic behavior and different results when the pipeline is run repeatedly. The pipeline addresses known concurrency issues with bowtie and samtools.


The SNP Pipeline uses multiple CPU cores during the bowtie alignment. Unless told otherwise, when bowtie runs multiple concurrent threads, it generates output records in the SAM file in non-deterministic order. The consequence of this is the SAM files and Pileup files can differ between runs. This may appear as two adjacent read-bases swapped in the pileup files.

To work around this problem, the pipeline uses the --reorder bowtie command line option. The reorder option causes bowtie to generate output records in the same order as the reads in the input file. This is discussed in the bowtie documentation here:


The SNP Pipeline runs multiple samtools processes concurrently to generate pileups for each sample. When the samtools pileup process runs, it checks for the existence of the reference faidx file, *.fai. If the faidx file does not exist, samtools creates it automatically. However, multiple samtools mpileup processes can interfere with each other by attempting to create the file at the same time. This interference causes incorrect pipeline results.

To work around this problem, the pipeline explicitly creates the faidx file by running samtools faidx on the reference before running the mpileup processes. This prevents errors later when multiple samtools mpileup processes run concurrently.

Software Versions

Different versions of the software packages this pipeline uses can generate different results. This is important to be aware of if you end up comparing the results between runs. We share our observations from the versions of SAMtools and Bowtie that we have used below.


Different versions of Bowtie can generate different SAM files, which subsequently causes different pileups and different variant detection. For example, with our included data sets, Bowtie 2.1.0 and 2.2.2 produce functionally identical SAM files when run on the Lambda Virus data set. However, the generated SAM files (and downstream results) are different when run on the Salmonella Agona data set.


SAMtools mpileup version 0.1.18 and version 0.1.19 differ in their default behavior. Version 0.1.19 can filter out bases with low quality, and by default, it excludes bases with quality score below 13 (95% accuracy). Version 0.1.18 does not have this capability, and thus different versions of SAMtools mpileup when run with the default parameters can produce different pileup files which can impact the snp list and snp matrix.

On one of our data sets with 116 samples, we observed these results:

  • 36030 snps found when pileups generated with SAMtools 0.1.18
  • 38154 snps found when pileups generated with SAMtools 0.1.19

Correct Results

As we have constructed our pipeline, we have found problems in our own software and in the various packages we use. To this point we have found one problem worth mentioning here.

SAMtools snp pileup difference from genome-wide pileup

An important processing step in the SNP Pipeline is creation of a pileup file per sample containing read pileups at the positions where snps were called in any of the samples. This pileup file should be a subset of the genome-wide pileup for each sample. However, the SAMtools software does not generate pileup records exactly matching the genome-wide pileup when given a list of positions for which the pileup should be generated. The differences are particularly evident at the first few snp positions and cause missing values in the SNP matrix. We first noticed this problem when the first or last position of the reference sequence was identified as a variant site. To work around this problem, the SNP Pipeline internally extracts the desired pileup records from the genome-wide pileup.

This SAMtools issue has been reported here:

Test Data Sets

We have created/curated a number of data sets for use in testing both the reproducibility and correctness of the pipeline. In the following sections we briefly describe these data sets.

Lambda Virus

This data set was built using the bowtie2 example, and intended to be a small test case and example that will run quickly and verify the basic functionality of the code.

Salmonella Agona

This data set was designed to contain realistic sequences, but not very many of them, so that it could be run in a reasonable amount of time. The data must be downloaded from the NCBI due to its large size. We provide a file of hashes that can easily be used to verify that the data downloaded matches the data originally used to produce our results. (Use sha256sum at the unix command line.)

Listeria monocytogenes

This is designed to be a realistic-sized data set based on an outbreak of L. m. in soft cheese. The data must be downloaded from the NCBI due to its large size. We provide a file of hashes that can easily be used to verify that the data downloaded matches the data originally used to produce our results. (Use sha256sum at the unix command line.)

Synthetic data sets

Coming soon in a future release

We are currently creating synthetic data sets based on simulating various evolutionary scenarios. The simulations are designed to be similar to what we would expect in the types of organisms we study (food-borne pathogens), with error structure appropriate for the platforms we use to do sequencing.