Srinka Ghosh

RNA Maps Reveal New RNA Classes and a Possible Function for Pervasive Transcription

Significant fractions of eukaryotic genomes give rise to RNA, much of which is unannotated and has reduced protein-coding potential. The genomic origins and the associations of human nuclear and cytosolic polyadenylated RNAs longer than 200 nucleotides (nt) and whole-cell RNAs less than 200 nt were investigated in this genome-wide study. Subcellular addresses for nucleotides present in detected RNAs were assigned, and their potential processing into short RNAs was investigated. Taken together, these observations suggest a novel role for some unannotated RNAs as primary transcripts for the production of short RNAs. Three potentially functional classes of RNAs have been identified, two of which are syntenically conserved and correlate with the expression state of protein-coding genes. These data support a highly interleaved organization of the human transcriptome.

Biological function of unannotated transcription during the early development of Drosophila melanogaster

Many animal and plant genomes are transcribed much more extensively than current annotations predict. However, the biological function of these unannotated transcribed regions is largely unknown. Approximately 7% and 23% of the detected transcribed nucleotides during D. melanogaster embryogenesis map to unannotated intergenic and intronic regions, respectively. Based on computational analysis of coordinated transcription, we conservatively estimate that 29% of all unannotated transcribed sequences function as missed or alternative exons of well-characterized protein-coding genes. We estimate that 15.6% of intergenic transcribed regions function as missed or alternative transcription start sites (TSS) used by 11.4% of the expressed protein-coding genes. Identification of P element mutations within or near newly identified 5′ exons provides a strategy for mapping previously uncharacterized mutations to their respective genes. Collectively, these data indicate that at least 85% of the fly genome is transcribed and processed into mature transcripts representing at least 30% of the fly genome.

Rank-statistics based enrichment-site prediction algorithm developed for chromatin immunoprecipitation on chip experiments

BackgroundHigh density oligonucleotide tiling arrays are an effective and powerful platform for conducting unbiased genome-wide studies. The ab initio probe selection method employed in tiling arrays is unbiased, and thus ensures consistent sampling across coding and non-coding regions of the genome. Tiling arrays are increasingly used in chromatin immunoprecipitation (IP) experiments (ChIP on chip). ChIP on chip facilitates the generation of genome-wide maps of in-vivo interactions between DNA-associated proteins including transcription factors and DNA. Analysis of the hybridization of an immunoprecipitated sample to a tiling array facilitates the identification of ChIP-enriched segments of the genome. These enriched segments are putative targets of antibody assayable regulatory elements. The enrichment response is not ubiquitous across the genome. Typically 5 to 10% of tiled probes manifest some significant enrichment. Depending upon the factor being studied, this response can drop to less than 1%. The detection and assessment of significance for interactions that emanate from non-canonical and/or un-annotated regions of the genome is especially challenging. This is the motivation behind the proposed algorithm.ResultsWe have proposed a novel rank and replicate statistics-based methodology for identifying and ascribing statistical confidence to regions of ChIP-enrichment. The algorithm is optimized for identification of sites that manifest low levels of enrichment but are true positives, as validated by alternative biochemical experiments. Although the method is described here in the context of ChIP on chip experiments, it can be generalized to any treatment-control experimental design. The results of the algorithm show a high degree of concordance with independent biochemical validation methods. The sensitivity and specificity of the algorithm have been characterized via quantitative PCR and independent computational approaches.ConclusionThe algorithm ranks all enrichment sites based on their intra-replicate ranks and inter-replicate rank consistency. Following the ranking, the method allows segmentation of sites based on a meta p-value, a composite array signal enrichment criterion, or a composite of these two measures. The sensitivities obtained subsequent to the segmentation of data using a meta p-value of 10-5, an array signal enrichment of 0.2 and a composite of these two values are 88%, 87% and 95%, respectively.

Abstract C10: Complete genome sequencing of complex tumors and matched normals results in sensitive detection of somatic events

For a comprehensive understanding of the tumor genome, it is important to identify all somatic event types: point mutations, small insertions and deletions, copy number aberrations, translocations, inversions, gene fusions, and more. Complete genome sequencing and analysis of paired tumor and normal samples yields a comprehensive view of the mutational landscape in the tumor. An integrated pipeline for the sequencing, computational analysis and annotation of complete human cancer genomes has been developed. Sequencing is performed on Complete Genomics DNA nanoarrays using a nonsequential, unchained read technology to generate 70base-pair paired end reads. Assembly, mapping, and variation calling are performed using methods that have recently been optimized for more sensitive detection of variants of low allelic fraction, as would occur in heterogeneous and/or aneuploid tumor genomes. Algorithmic enhancements designed to accommodate non-diploid genomes result in increased call rates and higher accuracy. Higher coverage levels also help to reduce error rates and improve the sensitivity of somatic variation detection. Approaches adopted to ensure a highly accurate and sensitive representation of tumor genomes will be presented. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the Second AACR International Conference on Frontiers in Basic Cancer Research; 2011 Sep 14-18; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2011;71(18 Suppl):Abstract nr C10.