637 research outputs found
Next-Generation Sequencing: Application in Liver Cancer—Past, Present and Future?
No full textHepatocellular Carcinoma (HCC) is the third most deadly malignancy worldwide characterized by phenotypic and molecular heterogeneity. In the past two decades, advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. More recently, improvements of sophisticated next-generation sequencing (NGS) technologies have enabled complete and cost-efficient analyses of cancer genomes at a single nucleotide resolution and advanced into valuable tools in translational medicine. Although the use of NGS in human liver cancer is still in its infancy, great promise rests in the systematic integration of different molecular analyses obtained by these methodologies, i.e., genomics, transcriptomics and epigenomics. This strategy is likely to be helpful in identifying relevant and recurrent pathophysiological hallmarks thereby elucidating our limited understanding of liver cancer. Beside tumor heterogeneity, progress in translational oncology is challenged by the amount of biological information and considerable “noise” in the data obtained from different NGS platforms. Nevertheless, the following review aims to provide an overview of the current status of next-generation approaches in liver cancer, and outline the prospects of these technologies in diagnosis, patient classification, and prediction of outcome. Further, the potential of NGS to identify novel applications for concept clinical trials and to accelerate the development of new cancer therapies will be summarized
Genome- and transcriptome-assisted development of nuclear insertion/deletion markers for Calanus species (Copepoda: Calanoida) identification
No full textCopepods of the genus Calanus are key zooplankton species in temperate to arctic marine ecosystems. Despite their ecological importance, species identification remains challenging. Furthermore, the recent report of hybrids among Calanus species highlights the need for diagnostic nuclear markers to efficiently identify parental species and hybrids. Using next-generation sequencing analysis of both the genome and transcriptome from two sibling species, Calanus finmarchicus and Calanus glacialis, we developed a panel of 12 nuclear insertion/deletion markers. All the markers showed species-specific amplicon length. Furthermore, most of the markers were successfully amplified in other Calanus species, allowing the molecular identification of Calanus helgolandicus, Calanus hyperboreus and Calanus marshalla
Accurate identification of polyadenylation sites from 3' end deep sequencing using a naive Bayes classifier
No full textErratum published to correct corresponding author details: Sheppard S, Lawson ND, Zhu LJ. Accurate identification of polyadenylation sites from 3' end deep sequencing using a naive Bayes classifier. Bioinformatics. 2014 Feb 15;30(4):596. doi: 10.1093/bioinformatics/btt714. Link to erratum on publisher's siteMOTIVATION: 3' end processing is important for transcription termination, mRNA stability and regulation of gene expression. To identify 3' ends, most techniques use an oligo-dT primer to construct deep sequencing libraries. However, this approach can lead to identification of artifactual polyadenylation sites due to internal priming in homopolymeric stretches of adenines. Although heuristic filters have been applied in these cases, they typically result in a high proportion of both false-positive and -negative classifications. Therefore, there is a need to develop improved algorithms to better identify mis-priming events in oligo-dT primed sequences. RESULTS: By analyzing sequence features flanking 3' ends derived from oligo-dT-based sequencing, we developed a naive Bayes classifier to classify them as true or false/internally primed. The resulting algorithm is highly accurate, outperforms previous heuristic filters and facilitates identification of novel polyadenylation sites. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online
Operations capability improvement of a molecular biology laboratory in a high throughput genome sequencing center
No full textThesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Vita.Includes bibliographical references (leaves 108-110).The Broad Institute is a research collaboration of MIT, Harvard University and affiliated hospitals, and the Whitehead Institute for Biomedical Research. Its scientific mission is to "(1) create tools for genomic medicine and make them broadly available to the scientific community and (2) apply these tools to propel the understanding and treatment of disease." The Broad Institute contains the world's largest high throughput genome sequencing center, which contributed approximately one third of the sequence for the Human Genome Project (HGP) completed in 2003. The Molecular Biology Production Group (MBPG) is the most upstream part of the Broad Institute's genome sequencing operation. This group is responsible for incoming DNA quality control, construction of DNA Libraries, and production of agar plates containing E.coli cell colonies (with many of copies of DNA). In this way, MPBG scales up raw DNA to a quality and quantity necessary for the subsequent high-volume, automated genome sequencing process. While most of the genome sequencing process at the Broad Institute had already been highly industrialized, MBPG had not yet undergone such a transformation and was still operated more like a laboratory than a manufacturing group.(cont.) This low level of operations capability resulted in a highly variable output from MBPG processes in terms of quantity, physical quality, and data quality. Additionally, the MBPG processes were not well understood or measured, yet had a very significant effect on downstream processes in the genome sequencing center. Thus, the goal of this thesis was to create a framework for improving the operations capability of a molecular biology laboratory in a high throughput genome sequencing center. This framework defined an operations strategy of maximizing quality in MBPG, characterized the group's sources of quality problems, implemented lean manufacturing and production forecasting in MBPG, and defined future opportunities for MBPG to implement Six Sigma and RFID. This thesis work resulted in significant quality improvements in MBPG as well as a much more industrial approach to the management of the laboratory's operations. More broadly, this thesis work can be applied to the operations capability improvement of any high-throughput laboratory in the biotechnology and pharmaceutical industry.by Matthew R. Vokoun.S.M.M.B.A
Scheduling of biological samples for DNA sequencing
No full textThesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 95-97).In a DNA sequencing workflow, a biological sample has to pass through multiple process steps. Two consecutive steps are hydroshearing and library construction. Samples arrive randomly into the inventory and are to complete both processes before their due dates. The research project is to decide the optimal sequence of samples to go through these two processes subject to operational constraints. Two approaches, namely, heuristic and integer programming have been pursued in this thesis. A heuristic algorithm is proposed to solve the scheduling problem. A variant of the problem involving deterministic arrivals of samples is also considered for comparison purposes. Comparison tests between the two approaches are carried out to investigate the performance of the proposed heuristic for the original problem and its variant. Sensitivity analysis of the schedule to parameters of the problem is also conducted when using both approaches.by Yuwei Hu and Chin Soon Lim.S.M
Control and optimization of Escherichia coli picking process for deoxyribonucleic acid sequencing
No full textThesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2004.Includes bibliographical references (p. 68).As part of its responsibility to the National Institutes for Health, the sequencing operation at the Broad Institute strives for cost-effective production. This thesis attempts to reduce variability in the sequencing operation's E. coli colony picking process-thereby improving efficiency-through the application of traditional operations improvement methodology. To achieve control over variability, the author first seeks to characterize the variability and identify its drivers, then to reduce the variability by manipulating the drivers, and finally to optimize productivity. The operations techniques utilized include fishbone cause-and-effect diagram, process flow diagram and organizational analysis. Several industrial statistical techniques such as control charting, linear regression, analysis of variance and designed experimentation are also heavily employed. Many factors were studied as candidate drivers of variability. Three criteria are used to discriminate among them: statistical significance, magnitude of effect on variability and controllability. The results show that one of the largest but least controllable factors is plate density, i.e., the number of colonies on a plate. Instead of attempting to control individual confounding factors in plate preparation, this thesis presents an alternative strategy for overcoming the plate density variability: introduction of a novel spotting process that allows for plate variability but still yields higher efficiency.by Julia L. Chang.S.M.M.B.A
Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale
No full textFull author list omitted for brevity. For the full list of authors, see article.Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce 'annotation principal components', multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol
Whole-genome analysis of diverse Chlamydia trachomatis strains identifies phylogenetic relationships masked by current clinical typing
No full textChlamydia trachomatis is responsible for both trachoma and sexually transmitted infections, causing substantial morbidity and economic cost globally. Despite this, our knowledge of its population and evolutionary genetics is limited. Here we present a detailed phylogeny based on whole-genome sequencing of representative strains of C. trachomatis from both trachoma and lymphogranuloma venereum (LGV) biovars from temporally and geographically diverse sources. Our analysis shows that predicting phylogenetic structure using ompA, which is traditionally used to classify Chlamydia, is misleading because extensive recombination in this region masks any true relationships present. We show that in many instances, ompA is a chimera that can be exchanged in part or as a whole both within and between biovars. We also provide evidence for exchange of, and recombination within, the cryptic plasmid, which is another key diagnostic target. We used our phylogenetic framework to show how genetic exchange has manifested itself in ocular, urogenital and LGV C. trachomatis strains, including the epidemic LGV serotype L2b
A genetic variation map for chicken with 2.8 million single-nucleotide polymorphisms
No full textWe describe a genetic variation map for the chicken genome containing 2.8 million single-nucleotide polymorphisms (SNPs). This map is based on a comparison of the sequences of three domestic chicken breeds (a broiler, a layer and a Chinese silkie) with that of their wild ancestor, red jungle fowl. Subsequent experiments indicate that at least 90% of the variant sites are true SNPs, and at least 70% are common SNPs that segregate in many domestic breeds. Mean nucleotide diversity is about five SNPs per kilobase for almost every possible comparison between red jungle fowl and domestic lines, between two different domestic lines, and within domestic lines--in contrast to the notion that domestic animals are highly inbred relative to their wild ancestors. In fact, most of the SNPs originated before domestication, and there is little evidence of selective sweeps for adaptive alleles on length scales greater than 100 kilobases
Origin and evolution of the bread wheat D genome
No full textBread wheat (Triticum aestivum) is a globally dominant crop and major source of calories and proteins for the human diet. Compared with its wild ancestors, modern bread wheat shows lower genetic diversity, caused by polyploidisation, domestication and breeding bottlenecks1,2. Wild wheat relatives represent genetic reservoirs, and harbour diversity and beneficial alleles that have not been incorporated into bread wheat. Here we establish and analyse extensive genome resources for Tausch's goatgrass (Aegilops tauschii), the donor of the bread wheat D genome. Our analysis of 46 Ae. tauschii genomes enabled us to clone a disease resistance gene and perform haplotype analysis across a complex disease resistance locus, allowing us to discern alleles from paralogous gene copies. We also reveal the complex genetic composition and history of the bread wheat D genome, which involves contributions from genetically and geographically discrete Ae. tauschii subpopulations. Together, our results reveal the complex history of the bread wheat D genome and demonstrate the potential of wild relatives in crop improvement.This research used the Shaheen supercomputer and the Ibex cluster managed by the Supercomputing Core Laboratory at King Abdullah University of Science and Technology (KAUST). We thank the systems administrators and computational scientists for help with debugging and overall support. The authors thank B. Steuernagel, D. Keyes, L. Fabbian and K. G. Kise for bioinformatics advice; E. P. Faig for greenhouse assistance; I. Walde for technical assistance with Hi-C library preparation and sequencing; H. Guo for administering
the OWWC sequencing; J. Poland and L. Gao for advice on PacBio sequencing; A. Bentley, B. Keller, H. Bürstmayr, J. Faris, M. Maccaferri, M. Bozzoli, R. Horsnell, D. Seung, J. Balk, R. McNelly and T. O’Hara for nominating Ae. tauschii lines of strategic interest; R. McIntosh for critical reading of the manuscript; E. Waller for assistance with the OWWC website; GrainGenes resources for hosting the online database; USDA for infrastructure support in terms of providing computational resources at SCINet high performance cluster; and University of Maryland supercomputing resources (http://hpcc.umd.edu) for computational work for the database. This publication is based on work supported by KAUST awards ORFS-CRG10-2021-4735 to B.B.H.W., ORFS-CRG11-2022-5076 to S.G.K. and URF/1/4352-01-01, FCC/1/1976-44-01, FCC/1/1976- 45-01, REI/1/5234-01-01 and REI/1/5414-01-01 to X.G.; Australian Government Research Training Program and the University of Queensland Centennial Scholarships to N.A. during genetic mapping of Lr39; Academy of Scientific Research and Technology (ASRT) project ID 19385 and Climate Change Adaptation and Nature Conservation (GREEN FUND-ASRT) to A.F.E.; National Major Agricultural Science and Technology project NK2022060101 and National Key Research and Development Program of China 2021YFF1000204 to L.M.; a Genebank3.0 project from the German Federal Ministry of Education and Research (grant no. FKZ 031B1300A) to J.C.R.; grants
from the Next-Generation BioGreen 21 Linked Program (PJ015786) of the Rural Development Administration (RDA), South Korea to J.-Y.L.; RDA/USDA-ARS cooperation agreement no. 58-0210-9-226-F to J.-Y.L. and Y.Q.G.; ARS project 2030-21430-015-000 to S.S.X. and Y.Q.G.;
the UK Biotechnology and Biological Sciences Research Council (BBSRC) Institute Strategic Programme Designing Future Wheat (BB/P016855/1) and a European Research Council grant (ERC-2019-COG-866328) to C.U.; the Mexican Consejo Nacional de Ciencia y Tecnología (CONACYT; 2018-000009-01EXTF-00306) to J.Q.-C.; funding from Department of Biotechnology, Government of India to P.C. and S.K.; USDA-NIFA Capacity Fund via South Dakota Agricultural Experiment Station to W.L.; a Bayer (previously Monsanto) Beachell Borlaug International Scholar’s Program to S. Ghosh; National Science Foundation of United States grant number 2102953 to J.D.; ARS Project 2030-21000-056-00D to G.R.L.; an USDA-NIFA Grant (2022-67013-36362) to V.K.T.; a NERC Independent Research Fellowship (NE/T011025/1) to L.T.D.; and 4D Wheat: Diversity, Domestication, Discovery and Delivery project (C.J.P.), funded by Genome Canada, Agriculture and Agri-Food Canada, Western Grains Research Foundation, Saskatchewan Ministry of Agriculture, Saskatchewan Wheat Development Commission, Alberta Wheat Commission, Manitoba Crop Alliance, Ontario Research Fund, and the Canadian Agricultural Partnership. Author contributions A.G.-
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