Architecture Overview

ADAM is architected as an extensible, parallel framework for working with both aligned and unaligned genomic data using Apache Spark. Unlike traditional genomics tools, ADAM is built as a modular stack, where a set of schemas define and provide a narrow waist. This stack architecture enables the support of a wide range of data formats and optimized query patterns without changing the data structures and query patterns that users are programming against.

ADAM’s architecture was introduced as a response to the challenges processing the growing volume of genomic sequencing data in a reasonable timeframe (Schadt et al. 2010). While the per-run latency of current genomic pipelines such as the GATK could be improved by manually partitioning the input dataset and distributing work, native support for distributed computing was not provided. As a stopgap solution, projects like Cloudburst (Schatz 2009) and Crossbow (Langmead et al. 2009) have ported individual analytics tools to run on top of Hadoop. While this approach has served well for proofs of concept, this approach provides poor abstractions for application developers. These poor abstractions make it difficult for bioinformatics developers to create novel distributed genomic analyses, and does little to attack sources of inefficiency or incorrectness in distributed genomics pipelines.

ADAM’s architecture reconsiders how we build software for processing genomic data by eliminating the monolithic architectures that are driven by the underlying flat file formats used in genomics. These architectures impose significant restrictions, including:

  • These implementations are locked to a single node processing model. Even the GATK’s “map-reduce” styled walker API (McKenna et al. 2010) is limited to natively support processing on a single node. While these jobs can be manually partitioned and run in a distributed setting, manual partitioning can lead to imbalance in work distribution and makes it difficult to run algorithms that require aggregating data across all partitions, and lacks the fault tolerance provided by modern distributed systems such as Apache Hadoop or Spark (Zaharia et al. 2012).
  • Most of these implementations assume invariants about the sorted order of records on disk. This “stack smashing” (specifically, the layout of data is used to accelerate a processing stage) can lead to bugs when data does not cleanly map to the assumed sort order. Additionally, since these sort order invariants are rarely explicit and vary from tool to tool, pipelines assembled from disparate tools can be brittle.
  • Additionally, while these invariants are intended to improve performance, they do this at the cost of opacity. If we can express the query patterns that are accelerated by these invariants at a higher level, then we can achieve both a better programming environment and enable various query optimizations.

At the core of ADAM, users use the ADAMContext to load data as GenomicRDDs, which they can then manipulate. In the GenomicRDD class hierarchy, we provide several classes that contain functionality that is applicable to all genomic datatypes, such as coordinate-space joins, the pipe API, and genomic metadata management.