computational genomics stanford

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computational genomics stanford

“An Interpretable Framework for Clustering Single-Cell RNA-Seq Datasets”, Jesse M. Zhang, Jue Fan, H. Christina Fan, David Rosenfeld, David N. Tse, 2018. This question has attracted a lot of attention in the literature, but as of now, there has not been a clear answer. 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. “One read per gene per cell is optimal for single-cell RNA-Seq”, M. J. Zhang, V. Ntranos, D. Tse, Nature Communications, 2019. 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. At the center, our group is closely involved in the Applications of these tools to sequence analysis will be presented: comparing genomes of different species, gene finding, gene regulation, whole genome sequencing and assembly. ~700 users. 350 Jane Stanford Way Stanford, CA 94305-9515, Tel: (650) 723-8121 paper) 1. We study the fundamental limits of this problem and design scalable algorithms for this. Recognizing that students may face unusual circumstances and require Stanford, CA 94305-9515, Helen Niu On the Future of Genomic Data The sequence and de novo assembly … Want to stay abreast of CEHG news, events, and programs? Medical genetics--Mathematical models. The past ten years there has been an explosion of genomics data -- the entire DNA sequences of several organisms, including human, are now available. Course will be graded based on the homeworks, 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. We also drew connections between this problem and community detection problems and used that to derive a spectral algorithm for this. Computational genomics analysis service to support member labs and faculty, students and staff. Electrical Engineering Department This is an instance of a broader phenomenon, colloquially known as “data snooping”, which causes false discoveries to be made across many scientific domains. Genetics Bioinformatics Service Center (GBSC) is a School of Medicine service center operated by Department of Genetics. State-of-the-art pipelines perform differential analysis after clustering on the same dataset. helen.niu@stanford.edu. Students are encouraged to start forming homework groups. The course will have four challenging problem sets of equal size Humans and other higher organisms are diploid, that is they have two copies of their genome. 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? This … 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. A mathematical framework reveals that, for estimating many important gene properties, the optimal allocation is to sequence at the depth of one read per cell per gene. Stanford University School of Medicine: Center for Molecular and Genetic Medicine The CSBF Software Library will be available 24/7. We considered this problem and firstly studied fundamental limits for being able to reconstruct the genome perfectly. s/he sees fit. Tech support will be available during regular business hours via e-mail, chat More about Cong Lab 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 problem here is to estimate which of the polymorphisms are on the same copy of a chromosome from noisy observations. The area of computational genomics includes both applications of older methods, and development of novel algorithms for the analysis of genomic sequences. Once these late days are exhausted, any homework turned in Many high-throughput sequencing based assays have been designed to make various biological measurements of interest. 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. thereof). The TN test is an approximate test based on the truncated normal distribution that corrects for a significant portion of the selection bias. Sequence alignments, hidden Markov models, multiple alignment algorithms and heuristics such as Gibbs sampling, and the probabilistic interpretation of alignments will be covered. However, we found that the conditions that were derived here to be able to recover uniquely were not satisfied in most practical datasets. Computational genetics and genomics : tools for understanding disease / edited by Gary Peltz. 350 Jane Stanford Way The most important problem in computational genomics is that of genome assembly. 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. Public outreach. We offer excellent training positions to current Stanford computational and experimental undergraduate, co-term, and masters students. Optionally, a student can scribe one lecture. Cong Lab is developing scalable CRISPR and single-cell genomics technology with computational/data analysis to understand cancer immunology and neuro-immunology. Room 310, Packard Building out. You must write the time and date of submission on the assignment. Includes bibliographical references and index. Summary In this thesis we discuss designing fast algorithms for three problems in computational genomics. Genome Assembly The most important problem in computational genomics is that of genome assembly. “Partial DNA Assembly: A Rate-Distortion Perspective”, Ilan Shomorony, Govinda M. Kamath, Fei Xia, Thomas A. Courtade, David N. Tse, 2016. Interestingly, the corresponding optimal estimator is not the widely-used plugin estimator but one developed via empirical Bayes. Currently 2800+ cores and 7+ Petabytes of high performance storage. 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. Stanford Center for Genomics and Personalized Medicine Large computational cluster. 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 Many single-cell RNA-seq discoveries are justified using very small p-values. Also, when writing up the solutions students should not use written notes from group work. Let us know if you need some help. Cancer Computational Genomics/Bioinformaticist Position - Stanford Situated in a highly dynamic research environment at Stanford University in the Departments of Me... Postdoc Fellows: DNA Methylation in Microbiome, Metagenomics and Meta-epigenomics We considered the maximum likelihood decoding for this problem, and characterise the number of samples necessary to be able to recover through a connection to convolutional codes. This course aims to present some of the most basic and useful algorithms for sequence analysis, together with the minimal biological background necessary for a computer science student to appreciate their application to current genomics research. 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). These must be handed in at the beginning of class on “Community Recovery in Graphs with Locality”, Yuxin Chen, Govinda Kamath, Changho Suh, David Tse, 2016. “Optimal Haplotype Assembly from High-Throughput Mate-Pair Reads”, Govinda M. Kamath, Eren Şaşoğlu, David Tse, 2015. and grading weight. Genomics The Genome Project: What Will It Do as a Teenager? Single-cell computational pipelines involve two critical steps: organizing cells (clustering) and identifying the markers driving this organization (differential expression analysis). GBSC is set up to facilitate massive scale genomics at Stanford and supports omics, microbiome, sensor, and phenotypic data types. In brief, every cell of every organism has a genome, which can be thought as a long string of A, C, G, and T. With current technology we do not have the ability to read the entire genomes, but get random noisy sub-sequences of the genome called reads. three days after its due date. the due date, which will usually be two weeks after they are handed 2019 Sep;14(9):866-873. doi: 10.1038/s41565-019-0517-8. Introduction to computational genomics : … 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. Students may discuss and work on problems in groups of at most three people but must write up their own solutions. We attempt to close the gap between the blue and green curves in the rightmost plot by introducing the truncated normal (TN) test. ISBN 1-58829-187-1 (alk. total of three free late days (weekends are NOT counted) to use as 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. Computational Biology Group Computational Biology and Bioinformatics are practiced at different levels in many labs across the Stanford Campus. Computational Genomics We develop principled approaches for both the computational and statistical parts of sequencing analysis, motivating better assembly algorithms and single-cell analysis techniques. Under no circumstances will a homework be accepted more than 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 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. Lecture notes will be due one week after the lecture date, and the grade on the lecture notes will substitute the two lowest-scoring problems in the homeworks. 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. If you have worked in an academic setting before, please add If you have worked in an academic setting before, please add … It is an honor code violation to write down the wrong time. NO FINAL. He joined Stanford in 2001. Computational design of three-dimensional RNA structure and function Nat Nanotechnol. Scribing. p. ; cm. 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? Many high-throughput sequencing based assays have been designed to make various biological measurements of interest. This cloud-based platform traverses biological entities seamlessly, accelerating discovery of disease mechanisms to address global public health challenges. “HINGE: long-read assembly achieves optimal repeat resolution”, Govinda M. Kamath, Ilan Shomorony, Fei Xia, Thomas A. Courtade, David N. Tse, 2017. Copying or intentionally refering to solutions from previous years will be considered an honor code violation. Late homeworks should be turned in to a member of the course staff, or, if none are available, placed under the door of S266 Clark Center. When writing up the solutions, students should write the names of people with whom they discussed the assignment. 2 Stanford Libraries' official online search tool for books, media, journals, databases, government documents and more. Electrical Engineering Department Use VPN if off campus. These are long strings of base pairs (A,C,G,T) containing all the information necessary for an organism's development and life. This event provided an opportunity for faculty, students, and SDSI's partners in industry to meet each A student can be part of at most one group. Students are expected not to look at the solutions from previous years. Durbin, Eddy, Krogh, Mitchison: Biological Sequence Analysis, Makinen, Belazzougui, Cunial, Tomescu: Genome-Scale Algorithm Design. Homework. Fax: (650) 723-9251 late will be penalized at the rate of 20% per late day (or fraction 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. This resulted in a rate-distortion type analysis and culminated in us developing a software called HINGE for bacterial assembly, which is used reasonably widely. Whenever possible, examples will be drawn from the most current developments in genomics research. “Valid post-clustering differential analysis for single-cell RNA-Seq”, Jesse M. Zhang, Govinda M. Kamath, David N. Tse, 2019. We observe that these p-values are often spuriously small. 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. Genomics is a new and very active application area of computer science. CS161: Design and Analysis of Algorithms, or equivalent familiarity with algorithmic and data structure concepts. Students with biological and computational backgrounds are encouraged to work together. First assignment is coming up on January 12th. During the first year, the center will present programs on "Genomics and social systems," "Agricultural, ecological and environmental genomics" and "Medical genomics." We introduce a method for correcting the selection bias induced by clustering. Stanford Libraries' official online search tool for books, media, journals, databases, government documents and more. These two copies are almost identical with some polymorphic sites and regions (less than 0.3% of the genome). The genome assembly problem is to reconstruct the genome from these reads. some flexibility in the course of the quarter, each student will have a Specific problems we will study include genome assembly, haplotype phasing, RNA-Seq quantification, and single-cell RNA-Seq analysis. Computer science is playing a central role in genomics: from sequencing and assembling of DNA sequences to analyzing genomes in order to locate genes, repeat families, similarities between sequences of different organisms, and several other applications. Serafim's research focuses on computational genomics: developing algorithms, machine learning methods, and systems for the analysis of large scale genomic data. The IBM Functional Genomics Platform contains over 300 million bacterial and viral sequences, enriched with genes, proteins, domains, and metabolic pathways. We studied the information limits of this problem and came up with various algorithms to solve this problem. Epub 2019 Aug … David Tse 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. The area of computational genomics includes both applications of older methods, and development of novel algorithms for the analysis of genomic sequences. To ensure even coverage of the lectures, please sign up to scribe beforehand with one of the course staff. Room 264, Packard Building If a student works individually, then the worst problem per problem set will be dropped. “Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts”, Vasilis Ntranos, Govinda M. Kamath, Jesse M. Zhang, Lior Pachter, David N. Tse, 2016. Extraordinary advances in sequencing technology in the past decade have revolutionized biology and medicine. (NIH Grant GM112625) Computational Genomics Extraordinary advances in sequencing technology in the past decade have revolutionized biology and medicine. Will Computers Crash Genomics? Hence we studied the complementary question of what was the most unambiguous assembly one could obtain from a set of reads. The research of our computational genomics group at Stanford Genome Technology Center aims at pushing the boundaries of genomics technology from base pairs to bedside. We observe that because clustering forces separation, reusing the same dataset generates artificially low p-values and hence false discoveries, and we introduce a valid post-clustering differential analysis framework which corrects for this problem. “Optimal Assembly for High Throughput Shotgun Sequencing”, Guy Bresler, Ma’ayan Bresler, David Tse, 2013. 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