Cloud Implementation of GT-FAR (Genome and Transcriptome-Free Analysis of RNA-Seq) University of Southern California.

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Presentation transcript:

Cloud Implementation of GT-FAR (Genome and Transcriptome-Free Analysis of RNA-Seq) University of Southern California

GT-FAR Pipeline

GT-FAR Components 1.Read Quality Control and Adaptor Trimming for Input Read File 2.Sequential Ungapped Mapping to Reference Gene-Models/Genome* 3.Gapped alignment to Reference Gene Models/Genome to faciliate Splice Variant Prediction* 4.Sample Quantification a)A reference based version concerning gene/junction/exon/pre-mRNA expression* b)A reference free quantification of read/kmer sequences 5.Output a)Quantification data, visualization, and an alignment sam file for further analysis b)Capable of including >99% in reference based output in high quality human samples 6.* When a reference genome and gtf file are available. If one is not available only a sequence/kmer based analysis (4b) is performed.

Pegasus WMS on the Cloud Allows scientist to design an analysis at a high-level without worrying about how to invoke it, execute it Provides Python, Java, and Perl APIs for workflow creation Automatically executes computations on computational resources available to the community or individual When failures occur, it tries to recover from them using a variety of mechanisms Records provenance Used in a number of domains: astronomy, bioinformatics, earthquake science, helioseismology, gravitational-wave physics, seismology, etc.. Detailed documentation on workflow design and execution at Pegasus tutorial on Amazon AWS User support available

GT-FAR Cloud Based Pipeline Investigators can start an EC2 instance with a GUI/GT- FAR Users can upload input files (FastQ file in gzip format) using web browser Tracks running workflows Users are able to download the outputs to their local laptops Outputs are also made available in Amazon S3 Allows for error reporting and debugging GT-FAR pipeline is available as a cloud-based solution hosted on Amazon EC2. ( ) The pipeline is executed on distributed resources using the Pegasus Workflow Management System ( ) Capabilities

GTFAR Success

GTFAR Failure

Expression of APOL1 APOL1 has moderate expression –we can notice that it all comes from a few exons and matching junctions –Hence, it is driven by a single transcript.

RNA-seq Analysis Workflows GT-FAR (Read-based RNA-seq Analysis) – New Functions: Novel Splice Junctions, Reference-free analysis – Pegasus WMS: – Pegasus GT-FAR (genome and transcriptome free analysis of RNA): – Pegasus tutorial on Amazon AWS – GitHub: RseqFlow (Standard RNA-seq Analysis) – Command line based – Functions: RPKM, Differential Expression, Variants – Google: – GitHub: – SourceForge: