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Articles by M. A. Marra
Total Records ( 2 ) for M. A. Marra
  M Krzywinski , J Schein , I Birol , J Connors , R Gascoyne , D Horsman , S. J Jones and M. A. Marra
 

We created a visualization tool called Circos to facilitate the identification and analysis of similarities and differences arising from comparisons of genomes. Our tool is effective in displaying variation in genome structure and, generally, any other kind of positional relationships between genomic intervals. Such data are routinely produced by sequence alignments, hybridization arrays, genome mapping, and genotyping studies. Circos uses a circular ideogram layout to facilitate the display of relationships between pairs of positions by the use of ribbons, which encode the position, size, and orientation of related genomic elements. Circos is capable of displaying data as scatter, line, and histogram plots, heat maps, tiles, connectors, and text. Bitmap or vector images can be created from GFF-style data inputs and hierarchical configuration files, which can be easily generated by automated tools, making Circos suitable for rapid deployment in data analysis and reporting pipelines.

  A. S Morrissy , R. D Morin , A Delaney , T Zeng , H McDonald , S Jones , Y Zhao , M Hirst and M. A. Marra
 

We describe a new method, Tag-seq, which employs ultra high-throughput sequencing of 21 base pair cDNA tags for sensitive and cost-effective gene expression profiling. We compared Tag-seq data to LongSAGE data and observed improved representation of several classes of rare transcripts, including transcription factors, antisense transcripts, and intronic sequences, the latter possibly representing novel exons or genes. We observed increases in the diversity, abundance, and dynamic range of such rare transcripts and took advantage of the greater dynamic range of expression to identify, in cancers and normal libraries, altered expression ratios of alternative transcript isoforms. The strand-specific information of Tag-seq reads further allowed us to detect altered expression ratios of sense and antisense (S-AS) transcripts between cancer and normal libraries. S-AS transcripts were enriched in known cancer genes, while transcript isoforms were enriched in miRNA targeting sites. We found that transcript abundance had a stronger GC-bias in LongSAGE than Tag-seq, such that AT-rich tags were less abundant than GC-rich tags in LongSAGE. Tag-seq also performed better in gene discovery, identifying >98% of genes detected by LongSAGE and profiling a distinct subset of the transcriptome characterized by AT-rich genes, which was expressed at levels below those detectable by LongSAGE. Overall, Tag-seq is sensitive to rare transcripts, has less sequence composition bias relative to LongSAGE, and allows differential expression analysis for a greater range of transcripts, including transcripts encoding important regulatory molecules.

 
 
 
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