A comparison of three programming languages for a full-fledged next-generation sequencing tool | BMC Bioinformatics

The sequence alignment/map format (SAM/BAM) [1] is the de facto standard in the bioinformatics community for storing mapped sequencing data. There exists a large body of work on tools for processing SAM/BAM files for analysis [1–15]. The SAMtools [1], Picard [2], and Genome Analysis Toolkit (GATK) [3] software packages developed […]

The sequence alignment/map format (SAM/BAM) [1] is the de facto standard in the bioinformatics community for storing mapped sequencing data. There exists a large body of work on tools for processing SAM/BAM files for analysis [1–15]. The SAMtools [1], Picard [2], and Genome Analysis Toolkit (GATK) [3] software packages developed by the Broad and Sanger institutes are considered to be reference implementations for many operations on SAM/BAM files, examples of which include sorting reads, marking polymerase chain reaction (PCR) and optical duplicates, recalibrating base quality scores, indel realignment, and various filtering options, which typically precede variant calling. Many alternative software packages [4–10, 12, 14, 15] focus on optimizing the computations of these operations, either by providing alternative algorithms, or by using parallelization, distribution, or other optimization techniques specific to their implementation language, which is often C, C++, or Java.

We have developed elPrep [8, 16], an open-source, multi-threaded framework for processing SAM/BAM files in sequencing pipelines, especially designed for optimizing computational performance. It can be used as a drop-in replacement for many operations implemented by SAMtools, Picard, and GATK, while producing identical results [8, 16]. elPrep allows users to specify arbitrary combinations of SAM/BAM operations as a single pipeline in one command line. elPrep’s unique software architecture then ensures that running such a pipeline requires only a single pass through the SAM/BAM file, no matter how many operations are specified. The framework takes care of merging and parallelizing the execution of the operations, which significantly speeds up the overall execution of a pipeline.

In contrast, related work focuses on optimizing individual SAM/BAM operations, but we have shown that our approach of merging operations outperforms this strategy [8]. For example, compared to using GATK4, elPrep executes the 4-step Broad Best Practices pipeline [17] (consisting of sorting, marking PCR and optical duplicates, and base quality score recalibration and application) up to 13x faster on whole-exome data, and up to 7.4x faster on whole-genome data, while utilizing fewer compute resources [8].

All SAM/BAM tools have in common that they need to manipulate large amounts of data, as SAM/BAM files easily take up 10–100 gigabytes (GB) in compressed form. Some tools implement data structures that spill to disk when reaching a certain threshold on random access memory (RAM) use, but elPrep uses a strategy where data is split upfront into chunks that are processed entirely in memory to avoid repeated file input/output [16]. Our benchmarks show that elPrep’s representation of SAM/BAM data is more efficient than, for example, GATK version 4 (GATK4), as elPrep uses less memory for loading the same number of reads from a SAM/BAM file in memory [8]. However, since elPrep does not provide data structures that spill to disk, elPrep currently requires a fixed minimum amount of RAM to process a whole-exome or whole-genome file, whereas other tools sometimes allow putting a cap on the RAM use by using disk space instead. Nonetheless, for efficiency, it is recommended to use as much RAM as available, even when spilling to disk [8, 18]. This means that, in general, tools for processing SAM/BAM data need to be able to manipulate large amounts of allocated memory.

In most programming languages, there exist more or less similar ways to explicitly or implicitly allocate memory for heap objects which, unlike stack values, are not bound to the lifetimes of function or method invocations. However, programming languages strongly differ in how memory for heap objects is subsequently deallocated. A detailed discussion can be found in “The Garbage Collection Handbook” by Jones, Hosking, and Moss [19]. There are mainly three approaches: Manual memory management Memory has to be explicitly deallocated in the program source code (for example by calling free in C [20]). Garbage collection Memory is automatically managed by a separate component of the runtime library called the garbage collector. At arbitrary points in time, it traverses the object graph to determine which objects are still directly or indirectly accessible by the running program, and deallocates inaccessible objects. This ensures that object lifetimes do not have to be explicitly modelled, and that pointers can be more freely passed around in a program. Most garbage collector implementations interrupt the running program and only allow it to continue executing after garbage collection – they “stop the world” [19] – and perform object graph traversal using a sequential algorithm. However, advanced implementation techniques, as employed by Java [21] and Go [22], include traversing the object graph concurrently with the running program while limiting its interruption as far as possible; and using a multi-threaded parallel algorithm that significantly speeds up garbage collection on modern multicore processors. Reference counting Memory is managed by maintaining a reference count with each heap object. When pointers are assigned to each other, these reference counts are increased or decreased to keep track of how many pointers refer to each object. Whenever a reference count drops to zero, the corresponding object can be deallocated.Footnote 1

elPrep was originally, up to version 2.6, implemented in the Common Lisp programming language [23]. Most existing Common Lisp implementations use stop-the-world, sequential garbage collectors. To achieve good performance, it was therefore necessary to explicitly control how often and when the garbage collector would run to avoid needless interruptions of the main program, especially during parallel phases. As a consequence, we also had to avoid unnecessary memory allocations, and reuse already allocated memory as far as possible, to reduce the number of garbage collector runs. However, our more recent attempts to add more functionality to elPrep (like optical duplicate marking, base quality score recalibration, and so on) required allocating additional memory for these new steps, and it became an even more complex task and a serious productivity bottleneck to keep memory allocation and garbage collection in check. We therefore started to look for a different programming language using an alternative memory management approach to continue developing elPrep and still achieve good performance.

Existing literature on comparing programming languages and their implementations for performance typically focus on specific algorithms or kernels in isolation, no matter whether they cover specific domains like bioinformatics [24], economics [25], or numerical computing [26], or are about programming languages in general [27–31]. Except for one of those articles [31], none of them consider parallel algorithms. Online resources that compare programming language performance also focus on algorithms and kernels in isolation [32]. elPrep’s performance stems both from efficient parallel algorithms for steps like parallel sorting or concurrent duplicate marking, but also from the overall software architecture that organizes these steps into a single-pass, multi-threaded pipeline. Since such software-architectural aspects are not covered by the existing literature, it therefore became necessary to perform the study described in this article.

elPrep is an open-ended software framework that allows for arbitrary combinations of different functional steps in a pipeline, like duplicate marking, sorting reads, replacing read groups, and so on; additionally, elPrep also accommodates functional steps provided by third-party tool writers. This openness makes it difficult to precisely determine the lifetime of allocated objects during a program run. It is known that manual memory management can contribute to extremely low productivity when developing such software frameworks. See for example the IBM San Francisco project, where a transition from C++ with manual memory management to Java with garbage collection led to an estimated 300% productivity increase [33]. Other open-ended software frameworks for processing SAM/BAM files include GATK4 [3], Picard [2], and htsjdk [34].

Therefore, manual memory management is not a practical candidate for elPrep, and concurrent, parallel garbage collection and reference counting are the only remaining alternatives. By restricting ourselves to mature programming languages where we can expect long-term community support, we identified Java and Go as the only candidates with support for concurrent, parallel garbage collectionFootnote 2, and C++17 [35] as the only candidate with support for reference counting (through the std::shared_ptr library feature).Footnote 3

The study consisted of reimplementations of elPrep in C++17, Go, and Java, and benchmarking their runtime performance and memory usage. These are full-fledged applications in the sense that they fully support a typical preparation pipeline for variant calling consisting of sorting reads, duplicate marking, and a few other commonly used steps. While these three reimplementations of elPrep only support a limited set of functionality, in each case the software architecture could be completed with additional effort to support all features of elPrep version 2.6 and beyond.

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