Automated DNA Sequencing System

Oak Ridge National Laboratory (ORNL) is developing a core DNA sequencing facility to support biological research endeavors at ORNL and to conduct basic sequencing automation research. This facility is novel because its development is based on existing standard biology laboratory equipment; thus, the development process is of interest to the many small laboratories trying to use automation to control costs and increase throughput. Before automation, biology Laboratory personnel purified DNA, completed cycle sequencing, and prepared 96-well sample plates with commercially available hardware designed specifically for each step in the process. Following purification and thermal cycling, an automated sequencing machine was used for the sequencing. A technician handled all movement of the 96-well sample plates between machines. To automate the process, ORNL is adding a CRS Robotics A- 465 arm, ABI 377 sequencing machine, automated centrifuge, automated refrigerator, and possibly an automated SpeedVac. The entire system will be integrated with one central controller that will direct each machine and the robot. The goal of this system is to completely automate the sequencing procedure from bacterial cell samples through ready-to-be-sequenced DNA and ultimately to completed sequence. The system will be flexible and will accommodate different chemistries than existing automated sequencing lines. The system will be expanded in the future to include colony picking and/or actual sequencing. This discrete event, DNA sequencing system will demonstrate that smaller sequencing labs can achieve cost-effective the laboratory grow

Growth of [lambda] variants with added or altered promoters in N-limiting bacterial mutants: Evidence that an N recognition site lies in the PR promoter

Transcription of the [lambda] genome, initiating at the early rightward promoter (PR), traverses the cII-O-P operon and extends through the Q gene. In the absence of the [lambda] N function, this transcription is prematurely terminated at either of two termination sites, tR1 and tR2. The cII-O-P operon lies distal to tR1, but proximal to tR2. A number of mutations resulting in new promoter activities (e.g., c17 and ric5b) mapping distal to tR1, but proximal to tR2, have been isolated.Although phages carrying the c17 mutation grow in a normal Escherichia coli host, we find that [lambda] derivatives carrying this mutation will not grow in mutant E. coli K12 hosts, Nus-, which limit [lambda] growth by inhibiting the expression of N function. However, under the same conditions, a [lambda] phage containing only the normal [lambda] promoters grows significantly better in the Nus- hosts. Our studies demonstrate that under conditions of limited N expression, phage carrying the c17 mutation can express functions coded for by genes in the cII-O-P operon, but not endolysin, a function coded for by a gene distal to tR2. Thus, under conditions of low N activity, functions whose genes lie downstream from the c17 promoter without any intervening termination signals are expressed. On the other hand, functions whose genes lie downstream from this promoter with an intervening termination signal are not expressed.These results are consistent with a model of N action, which has N acting only with transcription initiating at a specific class of promoters (e.g., PR), c17 not being a member of this class. Although previous studies (Friedman and Ponce-Campos, 1975) have shown that the ric5b promoter is also not a member of the N-utilizing class of promoters, we find that [lambda]ric5b grows on Nus- hosts. This suggests that whereas c17 interferes with transcription from PR, ric5b does not show such an interference.We also find that [lambda] variants carrying two mutations v3 and vs326, which map in the OR - PR region, exhibit the same growth characteristics in Nus- hosts as phages carrying the c17 mutation. These observations imply that the combination of v3 and vs326 interfere with N-modification of transcription initiating at PR, and lead us to conclude that one site for N recognition is located within the PR promoter.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/21867/1/0000271.pd

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

SeqAn An efficient, generic C++ library for sequence analysis

<p>Abstract</p> <p>Background</p> <p>The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome <abbrgrp><abbr bid="B1">1</abbr></abbrgrp> would not have been possible without advanced assembly algorithms. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there is a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use.</p> <p>Results</p> <p>To remedy this trend we propose the use of SeqAn, a library of efficient data types and algorithms for sequence analysis in computational biology. SeqAn comprises implementations of existing, practical state-of-the-art algorithmic components to provide a sound basis for algorithm testing and development. In this paper we describe the design and content of SeqAn and demonstrate its use by giving two examples. In the first example we show an application of SeqAn as an experimental platform by comparing different exact string matching algorithms. The second example is a simple version of the well-known MUMmer tool rewritten in SeqAn. Results indicate that our implementation is very efficient and versatile to use.</p> <p>Conclusion</p> <p>We anticipate that SeqAn greatly simplifies the rapid development of new bioinformatics tools by providing a collection of readily usable, well-designed algorithmic components which are fundamental for the field of sequence analysis. This leverages not only the implementation of new algorithms, but also enables a sound analysis and comparison of existing algorithms.</p

Alternative splicing and differential subcellular localization of the rat FGF antisense gene product

<p>Abstract</p> <p>Background</p> <p>GFG/NUDT is a nudix hydrolase originally identified as the product of the fibroblast growth factor-2 antisense (FGF-AS) gene. While the FGF-AS RNA has been implicated as an antisense regulator of FGF-2 expression, the expression and function of the encoded GFG protein is largely unknown. Alternative splicing of the primary FGF-AS mRNA transcript predicts multiple GFG isoforms in many species including rat. In the present study we focused on elucidating the expression and subcellular distribution of alternatively spliced rat GFG isoforms.</p> <p>Results</p> <p>RT-PCR and immunohistochemistry revealed tissue-specific GFG mRNA isoform expression and subcellular distribution of GFG immunoreactivity in cytoplasm and nuclei of a wide range of normal rat tissues. FGF-2 and GFG immunoreactivity were co-localized in some, but not all, tissues examined. Computational analysis identified a mitochondrial targeting sequence (MTS) in the N-terminus of three previously described rGFG isoforms. Confocal laser scanning microscopy and subcellular fractionation analysis revealed that all rGFG isoforms bearing the MTS were specifically targeted to mitochondria whereas isoforms and deletion mutants lacking the MTS were localized in the cytoplasm and nucleus. Mutation and deletion analysis confirmed that the predicted MTS was necessary and sufficient for mitochondrial compartmentalization.</p> <p>Conclusion</p> <p>Previous findings strongly support a role for the FGF antisense RNA as a regulator of FGF2 expression. The present study demonstrates that the antisense RNA itself is translated, and that protein isoforms resulting form alternative RNA splicing are sorted to different subcellular compartments. FGF-2 and its antisense protein are co-expressed in many tissues and in some cases in the same cells. The strong conservation of sequence and genomic organization across animal species suggests important functional significance to the physical association of these transcript pairs.</p

Moving toward a system genetics view of disease

Testing hundreds of thousands of DNA markers in human, mouse, and other species for association to complex traits like disease is now a reality. However, information on how variations in DNA impact complex physiologic processes flows through transcriptional and other molecular networks. In other words, DNA variations impact complex diseases through the perturbations they cause to transcriptional and other biological networks, and these molecular phenotypes are intermediate to clinically defined disease. Because it is also now possible to monitor transcript levels in a comprehensive fashion, integrating DNA variation, transcription, and phenotypic data has the potential to enhance identification of the associations between DNA variation and diseases like obesity and diabetes, as well as characterize those parts of the molecular networks that drive these diseases. Toward that end, we review methods for integrating expression quantitative trait loci (eQTLs), gene expression, and clinical data to infer causal relationships among gene expression traits and between expression and clinical traits. We further describe methods to integrate these data in a more comprehensive manner by constructing coexpression gene networks that leverage pairwise gene interaction data to represent more general relationships. To infer gene networks that capture causal information, we describe a Bayesian algorithm that further integrates eQTLs, expression, and clinical phenotype data to reconstruct whole-gene networks capable of representing causal relationships among genes and traits in the network. These emerging network approaches, aimed at processing high-dimensional biological data by integrating data from multiple sources, represent some of the first steps in statistical genetics to identify multiple genetic perturbations that alter the states of molecular networks and that in turn push systems into disease states. Evolving statistical procedures that operate on networks will be critical to extracting information related to complex phenotypes like disease, as research goes beyond a single-gene focus. The early successes achieved with the methods described herein suggest that these more integrative genomics approaches to dissecting disease traits will significantly enhance the identification of key drivers of disease beyond what could be achieved by genetic association studies alone

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN