Ariella Sasson
Children's Hospital of Philadelphia
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Publication
Featured researches published by Ariella Sasson.
Human Immunology | 2013
Curt Lind; Deborah Ferriola; Kate Mackiewicz; Anna Papazoglou; Ariella Sasson; Dimitri Monos
Many common and well-documented (CWD) HLA alleles have only been partially characterized. The DNA sequence of these incomplete alleles, as published in the IMGT/HLA database, is most often limited to exons that code for the extracellular domains of the mature protein. Here we describe the application of next-generation sequencing technology to obtain full length genomic sequence from a single long-range PCR amplicon for 15 common and well-documented HLA Class I alleles. This technology is well suited to fill in the gaps of the current HLA allele sequence database which is largely incomplete. A more comprehensive catalog of HLA allele sequences would be beneficial in the evaluation of mismatches in transplantation, studies of population genetics, the evolution of HLAs, regulatory mechanisms and HLA expression, and issues related to the genomic organization of the MHC.
BMC Bioinformatics | 2013
Zhe Zhang; Jeremy Leipzig; Ariella Sasson; Angela M Yu; Juan C. Perin; Hongbo M. Xie; Mahdi Sarmady; Patrick Warren; Peter S. White
BackgroundHigh-throughput sequencing (HTS) technologies are spearheading the accelerated development of biomedical research. Processing and summarizing the large amount of data generated by HTS presents a non-trivial challenge to bioinformatics. A commonly adopted standard is to store sequencing reads aligned to a reference genome in SAM (Sequence Alignment/Map) or BAM (Binary Alignment/Map) files. Quality control of SAM/BAM files is a critical checkpoint before downstream analysis. The goal of the current project is to facilitate and standardize this process.ResultsWe developed bamchop, a robust program to efficiently summarize key statistical metrics of HTS data stored in BAM files, and to visually present the results in a formatted report. The report documents information about various aspects of HTS data, such as sequencing quality, mapping to a reference genome, sequencing coverage, and base frequency. Bamchop uses the R language and Bioconductor packages to calculate statistical matrices and the Sweave utility and associated LaTeX markup for documentation. Bamchops efficiency and robustness were tested on BAM files generated by local sequencing facilities and the 1000 Genomes Project. Source code, instruction and example reports of bamchop are freely available from https://github.com/CBMi-BiG/bamchop.ConclusionsBamchop enables biomedical researchers to quickly and rigorously evaluate HTS data by providing a convenient synopsis and user-friendly reports.
Genetics in Medicine | 2018
Rashesh V Sanghvi; Christian Buhay; Bradford C. Powell; Ellen A. Tsai; Michael O. Dorschner; Celine S. Hong; Matthew S. Lebo; Ariella Sasson; David S. Hanna; Sean McGee; Kevin M. Bowling; Gregory M. Cooper; David E. Gray; Robert J. Lonigro; Andrew Dunford; Christine Brennan; Carrie Cibulskis; Kimberly Walker; Mauricio O. Carneiro; Joshua Sailsbery; Lucia A. Hindorff; Dan R. Robinson; Avni Santani; Mahdi Sarmady; Heidi L. Rehm; Leslie G. Biesecker; Deborah A. Nickerson; Carolyn M. Hutter; Levi A. Garraway; Donna M. Muzny
PurposeAs massively parallel sequencing is increasingly being used for clinical decision making, it has become critical to understand parameters that affect sequencing quality and to establish methods for measuring and reporting clinical sequencing standards. In this report, we propose a definition for reduced coverage regions and describe a set of standards for variant calling in clinical sequencing applications.MethodsTo enable sequencing centers to assess the regions of poor sequencing quality in their own data, we optimized and used a tool (ExCID) to identify reduced coverage loci within genes or regions of particular interest. We used this framework to examine sequencing data from 500 patients generated in 10 projects at sequencing centers in the National Human Genome Research Institute/National Cancer Institute Clinical Sequencing Exploratory Research Consortium.ResultsThis approach identified reduced coverage regions in clinically relevant genes, including known clinically relevant loci that were uniquely missed at individual centers, in multiple centers, and in all centers.ConclusionThis report provides a process road map for clinical sequencing centers looking to perform similar analyses on their data.
Genetics in Medicine | 2018
Sarah Sheppard; Sawona Biswas; Mindy H. Li; Vijayakumar Jayaraman; Ian Slack; Edward J. Romasko; Ariella Sasson; Joshua Brunton; Ramakrishnan Rajagopalan; Mahdi Sarmady; Jenica L. Abrudan; Sowmya Jairam; Elizabeth T. DeChene; Xiahoan Ying; Jiwon Choi; Alisha Wilkens; Sarah E. Raible; Maria I. Scarano; Avni Santani; Jeffrey W. Pennington; Minjie Luo; Laura K. Conlin; Batsal Devkota; Matthew C. Dulik; Nancy B. Spinner; Ian D. Krantz
PurposeHearing loss (HL) is the most common sensory disorder in children. Prompt molecular diagnosis may guide screening and management, especially in syndromic cases when HL is the single presenting feature. Exome sequencing (ES) is an appealing diagnostic tool for HL as the genetic causes are highly heterogeneous.MethodsES was performed on a prospective cohort of 43 probands with HL. Sequence data were analyzed for primary and secondary findings. Capture and coverage analysis was performed for genes and variants associated with HL.ResultsThe diagnostic rate using ES was 37.2%, compared with 15.8% for the clinical HL panel. Secondary findings were discovered in three patients. For 247 genes associated with HL, 94.7% of the exons were targeted for capture and 81.7% of these exons were covered at 20× or greater. Further analysis of 454 randomly selected HL-associated variants showed that 89% were targeted for capture and 75% were covered at a read depth of at least 20×.ConclusionES has an improved yield compared with clinical testing and may capture diagnoses not initially considered due to subtle clinical phenotypes. Technical challenges were identified, including inadequate capture and coverage of HL genes. Additional considerations of ES include secondary findings, cost, and turnaround time.
Gastroenterology | 2015
Judith R. Kelsen; Noor Dawany; Christopher J. Moran; Britt-Sabina Petersen; Mahdi Sarmady; Ariella Sasson; Helen Pauly-Hubbard; Alejandro Martinez; Kelly Maurer; Joanne Soong; Eric Rappaport; Andre Franke; Andreas Keller; Harland S. Winter; Petar Mamula; David A. Piccoli; David Artis; Gregory F. Sonnenberg; Mark J. Daly; Kathleen E. Sullivan; Robert N. Baldassano; Marcella Devoto
BMC Genomics | 2016
Johannes Dapprich; Deborah Ferriola; Kate Mackiewicz; Peter M. Clark; Eric Rappaport; Monica D’Arcy; Ariella Sasson; Xiaowu Gai; Jonathan Schug; Klaus H. Kaestner; Dimitri Monos
Human Genomics | 2015
Mindy Li; Jenica L. Abrudan; Matthew C. Dulik; Ariella Sasson; Joshua Brunton; Vijayakumar Jayaraman; Noreen P Dugan; Danielle Haley; Ramakrishnan Rajagopalan; Sawona Biswas; Mahdi Sarmady; Elizabeth T. DeChene; Matthew A. Deardorff; Alisha Wilkens; Sarah E. Noon; Maria I. Scarano; Avni Santani; Peter S. White; Jeffrey W. Pennington; Laura K. Conlin; Nancy B. Spinner; Ian D. Krantz; Victoria L. Vetter
Human Immunology | 2012
Curt Lind; Kate Mackiewicz; Jamie Duke; Ariella Sasson; Swati Ranade; Anand Sethuraman; Jason Chin; Jeff Robinson; Dimitri Monos
Human Immunology | 2013
Jamie Duke; Ariella Sasson; Kate Mackiewicz; Curt Lind; Endre Major; Tim Hague; Attila Berces; Dimitri Monos
Gastroenterology | 2014
Judith R. Kelsen; Christopher J. Moran; Ariella Sasson; Mahdi Sarmady; Kernika Gupta; Helen Pauly-Hubbard; Eric Rappaport; Catherine A. Stolle; Petar Mamula; Andrew B. Grossman; David A. Piccoli; Harland S. Winter; Robert N. Baldassano; Marcella Devoto