title: SeqPig: simple and scalable scripting for large sequencing data sets in Hadoop.
abstract: CONCLUSIONS
  Hadoop MapReduce-based approaches have become increasingly popular due to their scalability in processing large sequencing datasets. However, as these methods typically require in-depth expertise in Hadoop and Java, they are still out of reach of many bioinformaticians. To solve this problem, we have created SeqPig, a library and a collection of tools to manipulate, analyze and query sequencing datasets in a scalable and simple manner. SeqPigscripts use the Hadoop-based distributed scripting engine Apache Pig, which automatically parallelizes and distributes data processing tasks. We demonstrate SeqPig's scalability over many computing nodes and illustrate its use with example scripts.
  
  
  METHODS
  Available under the open source MIT license at http://sourceforge.net/projects/seqpig/
authors.affiliation: Aalto University School of Science and Helsinki Institute for Information Technology HIIT, Finland, International Computer Science Institute, Berkeley, CA, USA, CRS4-Center for Advanced Studies, Research and Development in Sardinia, Italy and CSC-IT Center for Science, Finland.
authors.forename: André
authors.initials: A
authors.lastname: Schumacher
authors.forename: Luca
authors.initials: L
authors.lastname: Pireddu
authors.forename: Matti
authors.initials: M
authors.lastname: Niemenmaa
authors.forename: Aleksi
authors.initials: A
authors.lastname: Kallio
authors.forename: Eija
authors.initials: E
authors.lastname: Korpelainen
authors.forename: Gianluigi
authors.initials: G
authors.lastname: Zanetti
authors.forename: Keijo
authors.initials: K
authors.lastname: Heljanko
mesh_terms: D057166:High-Throughput Screening Assays / Q000379:methods*; D012985:Software Design