“One read per gene per cell is optimal for single-cell RNA-Seq”, M. J. Zhang, V. Ntranos, D. Tse, Nature Communications, 2019. out. In brief, every cell of every organism has a genome, which can be thought as a long string of A, C, G, and T. Assistant Helen Niu 2 helen.niu@stanford.edu. We considered this problem and firstly studied fundamental limits for being able to reconstruct the genome perfectly. ~700 users. We attempt to close the gap between the blue and green curves in the rightmost plot by introducing the truncated normal (TN) test. Existing workflows perform clustering and differential expression on the same dataset, and clustering forces separation regardless of the underlying truth, rendering the p-values invalid. Students are expected not to look at the solutions from previous years. The IBM Functional Genomics Platform contains over 300 million bacterial and viral sequences, enriched with genes, proteins, domains, and metabolic pathways. It is an honor code violation to write down the wrong time. The past ten years there has been an explosion of genomics data -- the entire DNA sequences of several organisms, including human, are now available. Public outreach. ISBN 1-58829-187-1 (alk. If a student works individually, then the worst problem per problem set will be dropped. Recognizing that students may face unusual circumstances and require While several differential expression methods exist, none of these tests correct for the data snooping problem eas they were not designed to account for the clustering process. We study the fundamental limits of this problem and design scalable algorithms for this. Many high-throughput sequencing based assays have been designed to make various biological measurements of interest. Many single-cell RNA-seq discoveries are justified using very small p-values. Students may discuss and work on problems in groups of at most three people but must write up their own solutions. State-of-the-art pipelines perform differential analysis after clustering on the same dataset. Students are encouraged to start forming homework groups. An underlying question for virtually all single-cell RNA sequencing experiments is how to allocate the limited sequencing budget: deep sequencing of a few cells or shallow sequencing of many cells? Stanford Genomics The Stanford Genomics formerly Stanford Functional Genomics Facility (SFGF) provides servcies for high-throughput sequencing, single-cell assays, gene expression and genotyping studies utilizing microarray and real-time PCR, and related services to researchers within the Stanford community and to other institutions. A student can be part of at most one group. “Optimal Assembly for High Throughput Shotgun Sequencing”, Guy Bresler, Ma’ayan Bresler, David Tse, 2013. Room 264, Packard Building (NIH Grant GM112625) David Tse The TN test is an approximate test based on the truncated normal distribution that corrects for a significant portion of the selection bias. Homework. The most important problem in computational genomics is that of genome assembly. total of three free late days (weekends are NOT counted) to use as Computational Biology Group Computational Biology and Bioinformatics are practiced at different levels in many labs across the Stanford Campus. Single-cell RNA sequencing (scRNA-Seq) technologies have revolutionized biological research over the past few years by providing us with the tools to simultaneously interrogate the transcriptional states of hundreds of thousands of cells in a single experiment. GBSC is set up to facilitate massive scale genomics at Stanford and supports omics, microbiome, sensor, and phenotypic data types. This question has attracted a lot of attention in the literature, but as of now, there has not been a clear answer. Computational Genomics Extraordinary advances in sequencing technology in the past decade have revolutionized biology and medicine. p. ; cm. We also drew connections between this problem and community detection problems and used that to derive a spectral algorithm for this. This … Electrical Engineering Department Many high-throughput sequencing based assays have been designed to make various biological measurements of interest. s/he sees fit. The Computational Genomics Summer Institute brings together mathematical and computational scientists, sequencing technology developers in both industry and academia, and biologists who utilize those technologies for research applications. CS161: Design and Analysis of Algorithms, or equivalent familiarity with algorithmic and data structure concepts. A natural experimental design question arises; how should we choose to allocate a fixed sequencing budget across cells, in order to extract the most information out of the experiment? Founded in 2012, the Center for Computational, Evolutionary and Human Genomics (CEHG) supports and showcases the cutting edge scientific research conducted by faculty and trainees in 40 member labs across the School of Humanities and Sciences and the School of Medicine. Once these late days are exhausted, any homework turned in Computational design of three-dimensional RNA structure and function Nat Nanotechnol. More reads can significantly reduce the effect of the technical noise in estimating the true transcriptional state of a given cell, while more cells can provide us with a broader view of the biological variability in the population. Stanford Libraries' official online search tool for books, media, journals, databases, government documents and more. “Community Recovery in Graphs with Locality”, Yuxin Chen, Govinda Kamath, Changho Suh, David Tse, 2016. Copying or intentionally refering to solutions from previous years will be considered an honor code violation. Electrical Engineering Department These must be handed in at the beginning of class on When writing up the solutions, students should write the names of people with whom they discussed the assignment. At the center, our group is closely involved in the Program for Conservation Genomics | Stanford Center for Computational, Evolutionary, and Human Genomics Program for Conservation Genomics Enabling the use of genomics in conservation management The remaining major barriers to applying genomic tools in conservation management lie in the complexity of designing and analyzing genomic experiments. The course will have four challenging problem sets of equal size These two copies are almost identical with some polymorphic sites and regions (less than 0.3% of the genome). Hence we studied the complementary question of what was the most unambiguous assembly one could obtain from a set of reads. Epub 2019 Aug … We introduce a method for correcting the selection bias induced by clustering. He received a BS in Computer Science, BS in Mathematics, and MEng in EE&CS from MIT in June 1996, and a PhD in Computer Science from MIT in June 2000. More about Cong Lab If you have worked in an academic setting before, please add If you have worked in an academic setting before, please add … Optionally, a student can scribe one lecture. Stanford Data Science Initiative 2015 Retreat October 5-6, 2015 The SDSI Program held its inaugural retreat on October 5-6, 2015. However, this seemingly unconstrained increase in the number of samples available for scRNA-Seq introduces a practical limitation in the total number of reads that can be sequenced per cell. The best reason to take up Computational Biology at the Stanford Computer Science Department is a passion for computing, and the desire to get the education and recognition that the Stanford Computer Science curriculum provides. Computational genetics and genomics : tools for understanding disease / edited by Gary Peltz. Senior Fellow Stanford Woods Institute for the Environment and Bing Professor in Environmental Science Jonathan’s lab uses statistical and computational methods to study questions in genomics and evolutionary biology. paper) 1. Applications of these tools to sequence analysis will be presented: comparing genomes of different species, gene finding, gene regulation, whole genome sequencing and assembly. The Stanford Genetics and Genomics Certificate Program utilizes the expertise of the Stanford faculty along with top industry leaders to teach cutting-edge topics in the field of genetics and genomics. Let us know if you need some help. This is an instance of a broader phenomenon, colloquially known as “data snooping”, which causes false discoveries to be made across many scientific domains. Want to stay abreast of CEHG news, events, and programs? Fax: (650) 723-9251 STANFORD UNIVERSITY Introduction Dear Friends, Welcome to the Stanford Artificial Intelligence Lab The Stanford Artificial Intelligence Lab (SAIL) was founded by Prof. John McCarthy, one of the founding fathers of the field of AI. 350 Jane Stanford Way “Partial DNA Assembly: A Rate-Distortion Perspective”, Ilan Shomorony, Govinda M. Kamath, Fei Xia, Thomas A. Courtade, David N. Tse, 2016. Includes bibliographical references and index. Scribing. First assignment is coming up on January 12th. This event provided an opportunity for faculty, students, and SDSI's partners in industry to meet each three days after its due date. The genome assembly problem is to reconstruct the genome from these reads. late will be penalized at the rate of 20% per late day (or fraction The area of computational genomics includes both applications of older methods, and development of novel algorithms for the analysis of genomic sequences. some flexibility in the course of the quarter, each student will have a Students with biological and computational backgrounds are encouraged to work together. This cloud-based platform traverses biological entities seamlessly, accelerating discovery of disease mechanisms to address global public health challenges. We use Piazza as our main source of Q&A, so please sign up, The lecture notes from a previous edition of this class (Winter 2015) are available, A Zero-Knowledge Based Introduction to Biology, Molecular Evolution and Phylogenetic Tree Reconstruction. 350 Jane Stanford Way Stanford, CA 94305-9515, Helen Niu In this work, we develop a mathematical framework to study the corresponding trade-off and show that ~1 read per cell per gene is optimal for estimating several important quantities of the underlying distribution. “HINGE: long-read assembly achieves optimal repeat resolution”, Govinda M. Kamath, Ilan Shomorony, Fei Xia, Thomas A. Courtade, David N. Tse, 2017. We offer excellent training positions to current Stanford computational and experimental undergraduate, co-term, and masters students. African Wild Dog De Novo Genome Assembly We are collaborating with 10X Genomics to adapt their long-range genomic libraries to allow high-quality genome assemblies at low cost. Whenever possible, examples will be drawn from the most current developments in genomics research. Sequence alignments, hidden Markov models, multiple alignment algorithms and heuristics such as Gibbs sampling, and the probabilistic interpretation of alignments will be covered. Medical genetics--Mathematical models. Stanford, CA 94305-9515, Tel: (650) 723-8121 We observe that these p-values are often spuriously small. Interestingly, our results indicate that the corresponding optimal estimator is not the commonly-used plug-in estimator, but the one developed via empirical Bayes (EB). Serafim's research focuses on computational genomics: developing algorithms, machine learning methods, and systems for the analysis of large scale genomic data. Stanford Libraries' official online search tool for books, media, journals, databases, government documents and more. Interestingly, the corresponding optimal estimator is not the widely-used plugin estimator but one developed via empirical Bayes. “Valid post-clustering differential analysis for single-cell RNA-Seq”, Jesse M. Zhang, Govinda M. Kamath, David N. Tse, 2019. Stanford Center for Genomics and Personalized Medicine Large computational cluster. To ensure even coverage of the lectures, please sign up to scribe beforehand with one of the course staff. Extraordinary advances in sequencing technology in the past decade have revolutionized biology and medicine. Summary In this thesis we discuss designing fast algorithms for three problems in computational genomics. Welcome to CS262: Computational Genomics Instructor: Serafim Batzoglou TA: Paul Chen email: cs262-win2015-staff@lists.stanford.edu Tuesdays & Thursdays 12:50-2:05pmGoals of this course • Introduction to Computational and grading weight. 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