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Background: Technical improvements have decreased sequencing costs and, as a result, the size and number of genomic datasets have increased rapidly. Because of the lower cost, large amounts of sequence data are now being produced by small to midsize research groups. Crossbow is a software tool that can detect single nucleotide polymorphisms (SNPs) in whole-genome sequencing (WGS) data from a single subject; however, Crossbow has a number of limitations when applied to multiple subjects from large-scale WGS projects. The data storage and CPU resources that are required for large-scale whole genome sequencing data analyses are too large for many core facilities and individual laboratories to provide. To help meet these challenges, we have developed Rainbow, a cloud-based software package that can assist in the automation of large-scale WGS data analyses. Results: Here, we evaluated the performance of Rainbow by analyzing 44 different whole-genome-sequenced subjects. Rainbow has the capacity to process genomic data from more than 500 subjects in two weeks using cloud computing provided by the Amazon Web Service. The time includes the import and export of the data using Amazon Import/Export service. The average cost of processing a single sample in the cloud was less than 120 US dollars. Compared with Crossbow, the main improvements incorporated into Rainbow include the ability: (1) to handle BAM as well as FASTQ input files; (2) to split large sequence files for better load balance downstream; (3) to log the running metrics in data processing and monitoring multiple Amazon Elastic Compute Cloud (EC2) instances; and (4) to merge SOAPsnp outputs for multiple individuals into a single file to facilitate downstream genome-wide association studies. Conclusions: Rainbow is a scalable, cost-effective, and open-source tool for large-scale WGS data analysis. For human WGS data sequenced by either the Illumina HiSeq 2000 or HiSeq 2500 platforms, Rainbow can be used straight out of the box. Rainbow is available for third-party implementation and use, and can be downloaded from http://s3.amazonaws.com/jnj_rainbow/index.html. [ABSTRACT FROM AUTHOR]

Background: Technical improvements have decreased sequencing costs and, as a result, the size and number of genomic datasets have increased rapidly. Because of the lower cost, large amounts of sequence data are now being produced by small to midsize research groups. Crossbow is a software tool that can detect single nucleotide polymorphisms (SNPs) in whole-genome sequencing (WGS) data from a single subject; however, Crossbow has a number of limitations when applied to multiple subjects from large-scale WGS projects. The data storage and CPU resources that are required for large-scale whole genome sequencing data analyses are too large for many core facilities and individual laboratories to provide. To help meet these challenges, we have developed Rainbow, a cloud-based software package that can assist in the automation of large-scale WGS data analyses., Results: Here, we evaluated the performance of Rainbow by analyzing 44 different whole-genome-sequenced subjects. Rainbow has the capacity to process genomic data from more than 500 subjects in two weeks using cloud computing provided by the Amazon Web Service. The time includes the import and export of the data using Amazon Import/Export service. The average cost of processing a single sample in the cloud was less than 120 US dollars. Compared with Crossbow, the main improvements incorporated into Rainbow include the ability: (1) to handle BAM as well as FASTQ input files; (2) to split large sequence files for better load balance downstream; (3) to log the running metrics in data processing and monitoring multiple Amazon Elastic Compute Cloud (EC2) instances; and (4) to merge SOAPsnp outputs for multiple individuals into a single file to facilitate downstream genome-wide association studies., Conclusions: Rainbow is a scalable, cost-effective, and open-source tool for large-scale WGS data analysis. For human WGS data sequenced by either the Illumina HiSeq 2000 or HiSeq 2500 platforms, Rainbow can be used straight out of the box. Rainbow is available for third-party implementation and use, and can be downloaded from http://s3.amazonaws.com/jnj_rainbow/index.html.

Background: MicroRNAs (MiRNAs) are short non-coding RNAs that control protein expression through various mechanisms. Their altered expression has been shown to be associated with various cancers. The aim of this study was to profile miRNA expression in colorectal cancer (CRC) and to analyze the function of specific miRNAs in CRC cells. MirVana miRNA Bioarrays were used to determine the miRNA expression profile in eight CRC cell line models, 45 human CRC samples of different stages, and four matched normal colon tissue samples. SW620 CRC cells were stably transduced with miR-143 or miR-145 expression vectors and analyzed in vitro for cell proliferation, cell differentiation and anchorage-independent growth. Signalling pathways associated with differentially expressed miRNAs were identified using a gene set enrichment analysis., Results: The expression analysis of clinical CRC samples identified 37 miRNAs that were differentially expressed between CRC and normal tissue. Furthermore, several of these miRNAs were associated with CRC tumor progression including loss of miR-133a and gain of miR-224. We identified 11 common miRNAs that were differentially expressed between normal colon and CRC in both the cell line models and clinical samples. In vitro functional studies indicated that miR-143 and miR-145 appear to function in opposing manners to either inhibit or augment cell proliferation in a metastatic CRC model. The pathways targeted by miR-143 and miR-145 showed no significant overlap. Furthermore, gene expression analysis of metastatic versus non-metastatic isogenic cell lines indicated that miR-145 targets involved in cell cycle and neuregulin pathways were significantly down-regulated in the metastatic context., Conclusion: MiRNAs showing altered expression at different stages of CRC could be targets for CRC therapies and be further developed as potential diagnostic and prognostic analytes. The identified biological processes and signalling pathways collectively targeted by co-expressed miRNAs in CRC provide a basis for understanding the functional role of miRNAs in cancer.