AI News, bigdatagenomics/adam

bigdatagenomics/adam

ADAM is a genomics analysis platform with specialized file formats built using Apache Avro, Apache Spark and Apache Parquet.

A typical sequencing pipeline consists of a string of tools going from quality control, mapping, mapped read preprocessing, to variant calling or quantification, depending on the application at hand.

approach entails three main bottlenecks: We propose here a transformative solution for these problems, by replacing ad-hoc pipelines by the ADAM framework, developed in the Apache Spark ecosystem. ADAM

provides specialized file formats for the standard data structures used in genomics analysis: mapped reads (typically stored as .bam files), representation of genomic regions (.bed files), and variants (.vcf files), using Avro and Parquet.

This allows to use the in-memory cluster computing functionality of Apache Spark, ensuring efficient and fault-tolerant distribution based on data parallelism, without the intermediate disk operations required in classical distributed approaches.

This usually translates into (statistical) analysis of multiple samples, connection with (clinical) metadata, interactive visualization, using data science tools such as R, Python, Tableau and Spotfire.

These aliases call scripts that wrap the spark-submit and spark-shell commands to set up ADAM.Once they are in place, you can run adam by simply typing adam-submit at the command line, as demonstrated above.

For example, the following code snippet will generate a result similar to the k-mer-counting example above, but with the k-mers sorted in descending order of their number of occurrences.

ADAM relies on several open-source technologies to make genomic analyses fast and massively parallelizable… Apache Spark allows developers to write algorithms in succinct code that can run fast locally, on an in-house cluster or on Amazon, Google or Microsoft clouds.

Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language.

For this k-mer counting example, we filter out any records that are not mapped or have a MAPQ less than 20 using a predicate and only materialize the Sequence, ReadMapped flag and MAPQ columns and skip over all other fields like Reference or Start position, e.g.

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